{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# file header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/anaconda3/envs/CX/lib/python3.8/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "2025-03-18 10:01:39.954569: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-03-18 10:01:40.026959: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-03-18 10:01:40.027838: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-03-18 10:01:41.075247: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import sys,os,inspect\n",
    "if hasattr(sys.modules[__name__], '__file__'):\n",
    "    _file_name = __file__\n",
    "else:\n",
    "    _file_name = inspect.getfile(inspect.currentframe())\n",
    "CURRENT_FILE_PATH = os.path.dirname(_file_name)\n",
    "sys.path.append(os.getcwd()+\"/../neuronVis\")\n",
    "sys.path.append(os.getcwd()+\"/../resource\")\n",
    "from cx_tool import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '../resource/project_amygdala/'\n",
    "create_dir(data_path)\n",
    "amygdala_heirarchy = {'OLF':['COA'],'CTXsp':['LA','BLA','BMA','PA'],'sAMY':['AAA','BA','CEA','IA','MEA']}\n",
    "amygdala_region = []\n",
    "for i in amygdala_heirarchy:\n",
    "    for j in amygdala_heirarchy[i]:\n",
    "        amygdala_region.append(j)\n",
    "if not os.path.exists(data_path+'neuron_information.npy'):\n",
    "    amygdala_projection_neuron_name = []\n",
    "    for i in amygdala_heirarchy:\n",
    "        for j in amygdala_heirarchy[i]:\n",
    "            for k in iondata.getNeuronListByTerminalRegion(j):\n",
    "                if len(k['sampleid'])==6 and k['sampleid'][0]!='0' and len(k['name'])==7:\n",
    "                    neuron = k['sampleid']+k['name']\n",
    "                    if neuron not in amygdala_projection_neuron_name:\n",
    "                        amygdala_projection_neuron_name.append(neuron)\n",
    "    neuron_information = {}\n",
    "    for i in tool_bar(amygdala_projection_neuron_name,desc='generating neuron information...',leave=False):\n",
    "        control_console_output(0)\n",
    "        neuron_property = iondata.getNeuronPropertyByID(i[:-7],i[-7:])\n",
    "        neuron_information[i] = {}\n",
    "        neuron_information[i]['cortex_region'] = neuron_property['somaregion']\n",
    "        neuron_information[i]['axonal_projection_terminal_combined'] = get_axonal_statics_by_regions(i,amygdala_region,'terminalregion')\n",
    "        neuron_information[i]['soma_position'] = neuron_property['somapoint']\n",
    "        control_console_output(1)\n",
    "    np.save(data_path+'neuron_information.npy',neuron_information)\n",
    "else:\n",
    "    neuron_information = np.load(data_path+'neuron_information.npy',allow_pickle=True).item()\n",
    "    amygdala_projection_neuron_name = list(neuron_information.keys())\n",
    "_flatmap = cv.imread('../resource/flatmapedge2.tif',1)\n",
    "_flatmap_mask = cv.resize(cv.imread('../resource/flatmap.png',1),(_flatmap.shape[0],_flatmap.shape[1]))\n",
    "_flatmap_name = cv.resize(cv.imread('../resource/flatmapedge.png',1),(_flatmap.shape[0],_flatmap.shape[1]))\n",
    "flatmap_mask = np.zeros((_flatmap.shape[0],_flatmap.shape[1],4))\n",
    "flatmap_mask[...,:3] = (_flatmap_mask==0)*255\n",
    "flatmap_mask[...,3] = np.where(flatmap_mask[...,0]==0,0,1)\n",
    "flatmap = np.zeros((_flatmap.shape[0],_flatmap.shape[1],4))\n",
    "flatmap[...,-1] = np.where(_flatmap_mask[...,0]==0,1.0,flatmap[...,-1])\n",
    "flatmap[...,0] = 255-(np.mean(_flatmap_name,axis=-1)<200)*255\n",
    "flatmap[...,1] = 255-(np.mean(_flatmap_name,axis=-1)<200)*255\n",
    "flatmap[...,2] = 255-(np.mean(_flatmap_name,axis=-1)<200)*255\n",
    "flatmap[...,3] = np.mean(_flatmap_name,axis=-1)<200\n",
    "flatmap2 = np.zeros((_flatmap.shape[0],_flatmap.shape[1],4))\n",
    "flatmap2[...,-1] = np.where(_flatmap_mask[...,0]==0,1.0,flatmap[...,-1])\n",
    "flatmap2[...,0] = 255-(np.mean(_flatmap,axis=-1)<200)*255\n",
    "flatmap2[...,1] = 255-(np.mean(_flatmap,axis=-1)<200)*255\n",
    "flatmap2[...,2] = 255-(np.mean(_flatmap,axis=-1)<200)*255\n",
    "flatmap2[...,3] = np.mean(_flatmap,axis=-1)<200\n",
    "a,b = get_value_range(1-flatmap[...,0]/255)\n",
    "tmp = [np.mean(a),np.mean(b)]\n",
    "tmp2 = np.max([a[1]-a[0],b[1]-b[0]])\n",
    "flatten_map_r = [tmp[0]-tmp2/2,tmp[0]+tmp2/2,tmp[1]+tmp2/2,tmp[1]-tmp2/2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cortex->CEA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52141c2340744a7f8cf8b1b5df6d04f8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "iteration on neurons (generating shaft) ...:   0%|          | 0/24575 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terminal_thredhold = 5\n",
    "AMY = []\n",
    "for i in tool_bar(neuron_information,desc='iteration on neurons (generating shaft) ...',leave=False,colour='#55AF7B'):\n",
    "    if neuron_information[i]['axonal_projection_terminal_combined'][amygdala_region.index('CEA')]<terminal_thredhold:\n",
    "        continue\n",
    "    AMY.append(i)\n",
    "save_scene(AMY,[1,0,0],data_path+'CEA_AMY_th_'+str(terminal_thredhold))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_region = ['Isocortex','OLF','HPF','CNU','HY','TH','MB','P','MY']\n",
    "if 0:\n",
    "    mask = 0\n",
    "    for i,j in enumerate(source_region):\n",
    "        mask = np.where(get_region_mask(j)>0,i+1,mask)\n",
    "source_region = ['unknow']+source_region\n",
    "neuron_source = {}\n",
    "for i in AMY:\n",
    "    region = int(get_mask_value(mask,np.array(neuron_information[i]['soma_position'])/10))\n",
    "    if region not in neuron_source:\n",
    "        neuron_source[region] = []\n",
    "    neuron_source[region].append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isocortex 533\n",
      "unknow 181\n",
      "P 83\n",
      "HPF 36\n",
      "OLF 39\n",
      "MB 66\n",
      "CNU 423\n",
      "HY 801\n",
      "TH 87\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.pie([len(neuron_source[i]) for i in neuron_source],labels=[source_region[i] for i in neuron_source],radius=1,startangle=90,colors=[color_pool[i] for i in neuron_source],textprops={'fontsize':25})\n",
    "plt.pie([100],colors=['w'],radius=0.5)\n",
    "plt.title('Soma distribution of CEA-projecting neurons (n='+str(np.sum([len(neuron_source[i]) for i in neuron_source]))+')',fontsize=30)\n",
    "plt.savefig(data_path+'neurons_distribution_CEA.pdf',format='pdf')\n",
    "plt.show()\n",
    "for i,j in enumerate(neuron_source):\n",
    "    print(source_region[j],len(neuron_source[j]))\n",
    "    save_scene(neuron_source[j],color_pool[i],data_path+(source_region[j] if j!=0 else 'unknow'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.render.setPointSize(3)\n",
    "for i,j in enumerate(neuron_source):\n",
    "    neuronvis.clear()\n",
    "    X = []\n",
    "    Y = []\n",
    "    for k in neuron_source[j]:\n",
    "        X.append(neuron_information[k]['soma_position'].copy())\n",
    "        if X[-1][2]>5700:\n",
    "            X[-1][2] = 11400-X[-1][2]\n",
    "        Y.append(color_pool[j].tolist())\n",
    "    draw_points(neuronvis,X,Y)\n",
    "    for k in ['dorsal','left','anterior']:\n",
    "        set_view(neuronvis,k)\n",
    "        neuronvis.render.savepng(data_path+source_region[j]+'_soma_distribution_'+k+'.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in ['TH','Isocortex','HY','CNU']:\n",
    "    X = []\n",
    "    for j in neuron_source[source_region.index(i)]:\n",
    "        X.append(neuron_information[j]['axonal_projection_terminal_combined'])\n",
    "    X = np.mean(X,axis=0)\n",
    "    #X = [X[0],np.sum(X[1:-5]),np.sum(X[-5:])]\n",
    "    plt.bar(range(len(X)),X,color=color_pool[source_region.index(i)])\n",
    "    plt.xticks(range(len(amygdala_region)),amygdala_region)\n",
    "    plt.title(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insula, IT, CT, PT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da775797903e43418fb4e447711338b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generating neuron information...:   0%|          | 0/533 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cortex_region = ['FRP','PL','ILA','ORB','ACA','AI','GU','VISC','PTLp','RSP','TEa','ECT','PERI','MOs','MOp','SSp','SSs','AUD','VIS']\n",
    "# to facilidate using, also adding extra information\n",
    "brainstem_region_hierachy = {\n",
    "    'Cortex':['FRP','PL','ILA','ORB','ACA','AI','GU','VISC','PTLp','RSP','TEa','ECT','PERI','MOs','MOp','SSp','SSs','AUD','VIS'],\n",
    "    'OLF':['MOB','AOB','AON','TT','DP','PIR','NLOT','COA','PAA','TR'],\n",
    "    'HPF':['CA','DG','FC','IG','ENT','PAR','POST','PRE','SUB','ProS','HATA','APr'],\n",
    "    'Cortex-sp':['CLA','EP','LA','BLA','BMA','PA'],\n",
    "    'CNU':['STRd','ACB','FS','OT','LSX','AAA','CEA','MEA','PALd','PALv','PALm','PALc'],\n",
    "    'TH':['VAL','VM','VPL','VPM','VPLpc','VPMpc','PoT','SPA','SPFm','SPFp','PP','MG','LGd','LP','PO','POL','SGN','Eth','AV','AM','AD','IAM','IAD','LD',\n",
    "    'IMD','MD','SMT','PR','PVT','PT','RE','Xi','RH','CM','PCN','CL','PF','PIL','GENv','EPI','RT'],\n",
    "    'HY':['SO','ASO','PVH','PVa','PVi','ARH','ADP','AVP','AVPV','DMH','MEPO','MPO','OV','PD','PS','PVp','PVpo','SBPV','SCH','SFO','VMPO','VLPO','AHN',\n",
    "    'MBO','MPN','PMd','PMv','PVHd','VMH','PH','LHA','LPO','PST','PSTN','PeF','RCH','STN','TU','ZI','ME'],\n",
    "    'MB':['APN','NOT','NPC','PPT','RPF','AT','MPT','OP','VTA','SNr','SNc','SCs','SCm','NB','SAG','PBG','IC','RR','MRN','RN',\n",
    "    'PAG','PRC','INC','ND','Su3','CUN','PPN','IF','IPN','RL','CLI','DR','III','MA3','EW','VTN'],\n",
    "    'P':['PSV','PB','PCG','PG','PRNr','PRNc','SUT','TRN','V','P5','CS','LDT','NI','RPO','SOC','NLL','DTN','PDTg','SG','I5','SLC','SLD'],\n",
    "    'MY':['DCO','VCO','LAV','MV','SPIV','SUV','DMX','GR','CU','ECU','NTS','NTB','SPVC','SPVI','SPVO','Pa5','VII','GRN','IRN','LIN','LRNm','LRNp','MARN',\n",
    "    'MDRNd','MDRNv','PARN','PGRNd','PGRNl','PHY','VI','AMB','ICB','PAS','PRP','PPY','x','XII','y','IO','RM','RPA','RO'],\n",
    "    'CB':['VERM','HEM','FN','IP','DN','VeCB'],\n",
    "    'FT':['py','pyd'],\n",
    "    'SC':['unknow']\n",
    "}\n",
    "brainstem_region = []\n",
    "for a in brainstem_region_hierachy:\n",
    "    for b in brainstem_region_hierachy[a]:\n",
    "        brainstem_region.append(b)\n",
    "cortex_region_to_subregion,cortex_subregion_to_region = get_subregions(cortex_region)\n",
    "type = {}\n",
    "for i in iondata.getNeuronListBySomaRegion('GU')+iondata.getNeuronListBySomaRegion('VISC')+iondata.getNeuronListBySomaRegion('AI'):\n",
    "    type[i['sampleid']+i['name']] = i['type']\n",
    "Insula_CEA_info = {}\n",
    "for i in tool_bar(neuron_source[source_region.index('Isocortex')],desc='generating neuron information...',leave=False):\n",
    "    Insula_CEA_info[i] = {}\n",
    "    neuron_property = iondata.getNeuronPropertyByID(i[:-7],i[-7:])\n",
    "    Insula_CEA_info[i]['soma_position'] = neuron_property['somapoint']\n",
    "    Insula_CEA_info[i]['soma_hemisphere'] = 'left' if neuron_property['somapoint'][2]<5700 else 'right'\n",
    "    a,b = region_layer_seperate(neuron_property['somaregion'])\n",
    "    Insula_CEA_info[i]['cortex_region'] = cortex_subregion_to_region[neuron_property['somaregion']]\n",
    "    Insula_CEA_info[i]['cortex_subregion'] = a[len(Insula_CEA_info[i]['cortex_region']):]\n",
    "    if i in type:\n",
    "        Insula_CEA_info[i]['type'] = type[i]\n",
    "    Insula_CEA_info[i]['axonal_projection_length_combined'] = get_axonal_statics_by_regions(i,brainstem_region,'projectregion')\n",
    "    Insula_CEA_info[i]['axonal_projection_length_left'] = get_axonal_statics_by_regions(i,brainstem_region,'projectleftregion')\n",
    "    Insula_CEA_info[i]['axonal_projection_length_right'] = get_axonal_statics_by_regions(i,brainstem_region,'projectrightregion')\n",
    "    Insula_CEA_info[i]['axonal_projection_terminal_combined'] = get_axonal_statics_by_regions(i,brainstem_region,'terminalregion')\n",
    "    Insula_CEA_info[i]['axonal_projection_terminal_left'] = get_axonal_statics_by_regions(i,brainstem_region,'terminalleftregion')\n",
    "    Insula_CEA_info[i]['axonal_projection_terminal_right'] = get_axonal_statics_by_regions(i,brainstem_region,'terminalrightregion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = {}\n",
    "for i in Insula_CEA_info:\n",
    "    try:\n",
    "        if Insula_CEA_info[i]['type'][:2] not in tmp:\n",
    "            tmp[Insula_CEA_info[i]['type'][:2]] = []\n",
    "        tmp[Insula_CEA_info[i]['type'][:2]].append(i)\n",
    "    except:\n",
    "        pass\n",
    "for i in tmp:\n",
    "    save_scene(tmp[i],color_pool[0 if i=='IT' else 3 if i=='CT' else 1 if i=='PT' else 2],data_path+i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### insula to CEA neurons hemisphere differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gatw8/yt4+f4v4Otm9r3cYM0mwBU5v1+gYM3wfCN51ez6cQXwjpn9XlIHoHMLrIKfxZX90yVdbGZXV/B7c/o/pZyHUDAErU6uoFqmEGuAoWZ2ebrvnHTfuriS4PgK9/0N//i/kT6qOXih9zVgTrkGVYqjN7lCFm+YHY+P/hyc3DOFwlr4CrS7A4cCRzdUIeVG6srJng/n3Vw69wDGV5nmUr5vZvUFu6Tzcnk5WtI+pelvKSUV1BC88M0q7CVmNg4YJ+m8ksbGV+VrAGQNvsl4mufg2teFeOPtMCA/924YXuBBI42tNELwPl4RdUjyXC/pErwCyvM3/BkY3nCo6r3TsvME8yO090u6M5OVksaomf07V/HNTXFmleBsvFJdD+90rJe5VWqIlLh9G/gTBRPvS4FRwNvJ+yf4e/gK8HzJN3KAmR1VogAiyTiLKhoTZfKnbMXWyPvRLFKjoFOSe0kV8auMrGU7KG3RWUvx5y2AmjI/9J9m9pqkx2nCKtmlZZakJylugBf959OZ7j2LBsowSU9TaJiBd8xn4R3fzXOjTfOBy1K6a/AyJSt7m9SpKHkm+wGP42XKvBTvW7gCe3b+uZY03AfineRDgYOAVxswDc6X8ZdSsL7ogH8zmexLKNQpX8BH315Ksk3FFYgLgYfw7/17kr5nJeuilHxPhwD35Jyzzs97SeZ18frlwAay6GZccXk9kFmMNUl5VtrgxhvsX6JgQTeDgkXdO7lOWt5cuaFvpmynvZGyr1RpPSt3viPwOm7R07OB5OyWK296Ufx8KtEXL6uzkdR6ixsta5qd/VdUGDaQjjVy9fh5krasJpwWkn8Gvy1xqx/MATpUKv8asWLcMl3bAn9XJ+JtonENKHwex9/v3WlCWZB75zJ58mX6zfi7fz1uhZAv48+QtJSCsuyVkk57HfDXJNPACvFXeif3kmQpjDVZdkrWhXieGt5uLlcfHVhSN80uKVMr8Qn+3X4dGJZTiu4A/IDC93w8sDc+wDChJP6dzewySTOTe03+Wy95P5syfW8WXiYKHzzLKxzux5W+NXgb5st4e2cd4AxcSf0XYDv5YI3hZfAZJX7H4t/+2nhdlH1b5eq4Btv0zf3WW0Ij7Y7nJG2U0tfdzJ5P95zVxAG7Kfg78s+8ciHF/Sm8vb6Wmf0EV+wYPsDRIKFgCNqCbASlK94gKSJ9nOAv52BJX03HQ/EP5FP4SEwl3jGzeyWdjBfkt1PoMJ6NVybXp9+oXJy7A+MoFLKd8cLpMeAWvIDIFAED8Qbqf9LvW7lCdlhJZbIPcHeS/cnShlGKOwvn8BTfp/CGzeJ0/GgjaS7tlG0l6dDk9DDeqDs15cEnVDmnX00z3fskddrPTHLn4xid4le69gmFEYtvA5/GO/89kvxP4Pk2L4XzMfDbkobsrvnGVrqnHEtTAydTKMxLYc3DFUf5jvGuFN6XrXDLmWreu9qU/t2AhZKyvP0YeDTXKDkvyVBv/i0pWwRnCf6er0WhEvyqFaaK5K0ZzsDz8ingigZGOfONv8Xk1lwAvmZm/8o8pm/gY7zBfVa65xi8UvlaSQPi9+meM5OsYyg0NsiFWbr40nu4Ao3G8rKkMdoB71x2A3qWdE4aU1Lmw7xObsn0KrBLIwqH2/H3ohuwoCQtB+Df5o5Jrt/k4ig1wW9WY0KFkUoBL+PWJZkF0JwmBNUnV74NAu6qMv4z8Abp0cDLpekysy3LpTO99xfgFlRHAk/j38+MEqXb4cCJ+HzNU/DRqCzPFwEfSfo6/v1Dmc5bY+Rk/wj/tn+PP/cewLV4x/By/HvqRaHjmW8498Y7/A/iz+RLSbbM0mlN/Bv7evLzWEr/1/CFw36Yk308Xo88ir+L2fHTeKN5Bl4vfAYvHzKriVI+wb8P8O+rfo4yPgJcPy3CzEYASOpZ2hhN9zyDd6B7qRnTw0qUSn/Ay4OJeGfgQOBCfEX3eSlv51EwKb+M4s5dnrKddpbt3Obr3P1KlJJF097M7I8pP85pIDkz8Pf3crxzdRvwv8B/G8mGPYGf422G0gXlRHF9mB+xv0a+8j1QUZk5BK/Hv4U/s84l4RTlXa7MWqZTqiYo5/H3cA7+DE6UdDuFsvdzkjYBjgM2oGAttcx0HPmK/Q1aMeLPdjb+LbyKW5sKbxPtl8uDXfD39BHc+q4pzKX4G8nzXfy9PALP54cplAWv4992pizbM9dp7w7ciJdjojBy2xBbl3T+8+f742WR4VZV1+Jl5smpwzsPuMUaNk3PT1nchOK21E0Ul6mVqAE+h3+zz5ek+Sa8fJqH13934pYMhn/rpYMM9yT3t5Kfl3FLtTxF0/dK31cKZRR4ef0vvEz6gGKFwxDgl/jUCOHlzVHARngdswde3/wCV7bOxwdpvpKOM7+74e2Kv+BWpdnUl5eAffOCpzpuCV7uriFpBP5+WcqfiZT/1qH1lQ2lCrI8PfAyaTRwuKT98Gf8a0k9zOySKuN4Ef8mkLSNmU2F+vr1u2b2G0nfTH774+/K53DF/DKEgiFoC94HTsMbkw3NV+tNQUv+NPA9/KNZL9dBO7v0plyFKWCQpNkpri3xDmxm9k4K83J8obte6fq+6fcEhUK2L/BcarQswQurTBHQD+hiZrel+L+WZP8s0AdYO9fA2bekcn+AYu30ublwvpJL5+jc8TKKgNLOP17IZ3MBz8AbuAZ8H9+iJ+uMr4FXptkIeT7M0o5NkeleqQwlLEyNny/gncL8KPyeeEX8U7xiGU9hxKIHbimSjYwfnfJyevplnGpmp+casj+V9AmFxlYljpCb9c9L8f2VgslzqfnhX/AO04XAlxt57/LTePpLGohXeOua2fmpM9U7J2s/vLI5j4L5d0e8kZxZKeyLV0rr4RXiI0kDDd7R+CDn9tMk5/clzcE7+Zniqn4hR7zBe0VyfwGvaLbEzTonpHDn4Bro76TO/XX4aNF95BoQku5IsozFR3Ozjne9dUKidOTrx/g7AP5O5julReazeEMza4zOBv5lZs+lDkGHpMj5IV7J559HWfN83Oy8A97Qv9LM3itROORHuLqneBfjHY5/5NJyHq6ovM5KFjBKFexCCib4VVPS8TvCzA5M18/Dt3rKLIC+V2U47+MNp9dIDUX5SNi7yb3UDDbfQfs83oj8FbCPSqYWlJyPUW5dELxxvAfwHDDezK5L4Z+t4mkRY/B36s4kJ/h7m+X5E3hZ/Rv8m9wdf99/JukpcqbieDnS4ChRTtZd8Y7/AmC4mV0iaTg+MncnsLuZnZfuOQdXgn8aN9neEv/m7sTrgTNzsn0va6RJ6poaXFvj3+ldePn380x2vON1GN4pm4or9I7Dy6IBeHn2dfy97EiympA0nmLFwG34d/J1vEH/efw7PSnlXzYtYnGuU3oAXu9+HS9jTgc+sGRqroJCX/g85iwPlzFHV/E0r6/ijfLzU9wfmdmlyd/3zWyd9L1emWRcQsEEvrRzl2fPlGc/BbKRUfBOcWnntlOuw7YP/k4dmfL0HxQ6jHlFwSMlysMdzOw0SV3x97B3+v0dV+iUpj//PMan57AA+EuJe7/0rLL6MBux3xwvdyeROk8l8pyEK8bWxzsIR5VYJg7HO6Jb4O2OPKPwd/TXQOmc8iLlvKS9KT+QsCdwAv4c+qWy96yU3/lO4bZUno6TL8NuL1EUv05BqXBn8n9oypuFFDrNi3Dl7gK8TCiLSqaT4O/WvUnWr+F1GwBmdmlSgEzErZlqckqoW/H37yO8bjYKSs+d8e/woXR+FV63NmQBtBveCc46//nzDmZWm+JbDGyaPedUFo0DrpBbou2T5BRpyooKUxbfo9CWEl6PZWXqeipYJUxN//n86ZRrh76Id1ozhUK+nvsp/q79zszmJv+ZxcIESffibfffpXzqn2QeVfyEOBk4FX8HeiT3rN31PYoVqFfi5feTKU2jcIX5MODNFM5ovGy+PaVrHN5+zBQIQ8xsuKTf4eX3T3DlQeb3HeDPFJQR2+OK2lvx/kKeuyi0H3+Ll2HX4e9GX4rbvnnrnIUpTybRgvVFGih7luBl6JHAoZLmU2xB8QneZv4A/w5HpjKuS4U4SvsXS3Jl+g/xdwhJP0n/Z1Iog3ok993Khm/telpJsDKSXkzDP+Z18QIs37H4h6W5WMkveINgqJmdka7vaWZPlYR7LgVzq1HA0+YL2W0DHFhSKV+BF0pPAReZ2RclnYh/FJ1zja1zcA1nj/R/sJkdltzOxjtq26Rg98W1tVfgGuP+uPn5ILziHU1Ba551iLPG2aRcOH1x7Sx4pZqZsL9vZlnFm8lWm5kqpYb7a/hoYCe8UP1iiuuHuHb1Q7yRcxBeoU/GFSjv58IdY2aH5vK0c0q74aN02Tzo9fHC/a6c2zvp+pN4YfopfAT8ObzCyPJ8H7yBsAWu3RyCNwozJcGGeOV0dsq32hTHiSmOi0mjo2a2d072C8l1ntLlzKzwADM7Kuf3wJS/u+MN6UcoFMgd8IppKbClmf043TOMnDIhxTE/11D7O/5OXJbS+Cm8on8KODRpeM9Mz4d0fgJeMXTJNSi+W/K+Hog3Eg4B/l3i1h3X3j+e8uSvFK898iN8dPRcXFG3E/BG+v8bsJ+Z/SqngT7RzG5Kcq2Jvzc341sOrYE35P+RyWNmp6d4zkzPby6F73lb/JvYOD2XOfgo9u2SfoA3FPJKifVSOg7GFVtZZXYr8IiZ/Z+kL+JKtIfwynQm3pipnwuZZJmOfwPjc8dPUmgM90kybUz6DvBO0sTkfij+jYB3MO7B35WNcUXm/el/a/wdzdK8b0pDXQpzZ6qcWpAq/Ovxfbtvwxs2Ss/uRAplwRcoKONewRsw+bAzReMjSbYr8XJW+CJWv055NAz/PrP7Oqb0z8JHgR7ERxqn46NR6+XStUnJ+UF4o+9R/NvNGmq748+ZlD8b4t/zLLxxth/+TvWgeKpS7/ReDsE7OO/j72OPJOtHVjx97o5cmq9JzytL19ScrJ/BFcT1FiuSvpxk+IyZnZmubW9mL6XjA/BytS75WyMpJ/Ky9U5y98MbZTfj79zHKZ3HUfh+3kjPohf+zl2X7t8tHYOX3c/g72y+IXZ8yuOz8edsye/f0/HWeGOzP/49XorXf39NaTkaL8NqU9xH4HVeltYv4PWI4XOTszpn/3z5mfyuT0Hp9hzeuXw0hfshrsypA/ZJHbid8DLs6ZSf/fH38Re5cuoSvE7Jf8/fTHn3RTPbJ8V9Dl6HkGTdHy97F+Df3E74+/hkuv7tXL3+V1w5+lkzO1pSbwrTNx7CBwlew8urD5LMAPeW1JVd8VHDH+CdhdH4u5DVPe9T+J5vwOu4ybiy81D8u/0n3ql6moKJ+G05eT6Hd4rrUqfg2HRd4NYO6f2tz8uSZ5S9o1un55R9E8fhbRXDv9N5+baEmf0uF0a+jvkBbs3ZK+XP3zNvuCLn8xTeu1/hZWwW5/9RWFNhDq7kejzl7//i385huPn9JPw9WIyXIZfjZd7O+PebpT+z/MtkraSk7ZGeh+GDFReWpHF/3CrtJrycz9pk25hZg5aeKb7pFOqVS/H2ySzc2i9fV5+aKVtLz9O3dHAK515gJzO7N7kdjNdFZ+HfVDe8HHmHgjItY228zu2Mt6+zUXXwd+6rVpiSgBVbgD5gZpPT+UUpfFi27fQV/PveCleMXC1f42s4Xo5di39DW+Hl+1h8sOoRii1310hB/jfJ2gdvIy5N7s/mZB9Ceu5m9rikR/F360m8HpuBf0ub4Z342em+LfHy9i28TrgQf+f6Jz/b5/zuibfVNkrxZc/8a8Afrdjic3jyt3uS93Uz+1Vy+zNe50yh8K1nbd0Dk3xPAceZ2fBcmGfh9dhMfPeFbDAmc8/a8YZ/R5viZct3zez7uTL0OLyuHAGcg1sevJMLaomZjUtK8GNSvi5jDSrpzJL+RU3uff0T/qzm4M/u2fS/yMz+KF+X6NPAjmb2NxogFAxBq5M0qIsojJI8RqFh+F28gzQUr2C2Av6dbt0ZbzxMwj/e3SkenbwCrxwuwkfvH0rh98A/ujngFZJ8lLl3unYoXuAsMp+rvF2ukN3ezF7KaTxvwBvg4A3VRSmMvfDOWge8IXCzpKvwCvEKvNF4X7qvPzDBiqdwbEJhwcE98EZd95TGbGrE2hQUD4Y3Xm/FKxvDG5WdUj6NTzI9lvLgvpTOd3FN7REpXx83sxoVT0s5E6+EJ+AF2EC80Wf4CPTluEZ7EF6JHJhzy1f2h+MjkJmC5k680/BiGlnYEK9Ysg7U3SnMznjDdkf8We+e0j4VmGhm/8kVpL/ClT7z0vPKOslZZ1f4KPO8VIHeZWavJ79n4+/cJ/gobEe8U/AdvLH36STfwSkvBqX8/YOZPSnpu3gF+Qle2WbP5Cy8gjgmyZC9o6RCfUC6b7+SZzAMf18yU8LJ+CjTM6kR0xmvLP+CV2hTU7pG4B2Yj/CG/Tz83f5eegadU14uxiu2jfDv6Hd4o/MVM/tE0u54xf+CmT2Te45I+kuSZyr+rRyVwt0ZNyWGwvcEXiE/jDc0fpWurYdXdF3M7BFJ21EwJcxGbG/EG3m1eMM1M4HthzcYhVsl/DvlefZN9MYru9Pwd31pcjs/PY/M3PxneGesFv8WdqbwTfXAGxUXJ3luxhua4I2DY1NaDfiZmfVLeXME/r1laf5u6rQI+KmZXSy3krgRNx0/lEKn45aU7ux9WS8dn5rS8QmFztsm+Ij6Yfio1dAU/3l4hyTreJ6DN+QzRWOflDf9UwflNLwB/1JK78d4p+F3eAPov3hZcgo+wmO40iRT6HbEy6gPUh4uxBuWH+DvxDgKSrid8fLpBQrlwB3pOa6Nlx1j8RH9DhRMeDfHO3hLKSgd10l5Y/j3uS+FhuFAvMH9REpzln+P4srV36T7svJxPvCWmd2dntOuSYY9ceXZfun/1ZQWS3m/JB2/mp5HJttiCiNB2Tv6EfA/yf9ICiOnWVmWlS8Pp7Rkz2c7vHxYgDd+P8S/47ty9/8CL4e7UijDPk+hkzOjRBk9Kj2XrOF8VcqzJUkJf0ryul+Kd2w6PwZ/XuDPZ0f83f9vUvS9RkHpth7+Hu4EvGFmd6S0KKVnZ7wTAG6N+Lsk2zn4+9GZgvJzAP7OnmlmB+TScQveSajDy4QleIP7H0mGTSgouLdK/jKm4e9wV/ydmAFsYWZnyufXn4+XCx2S/8fS/8/wTn5Wx9VQaORn5taP4VYwv6TwjYKXUzPxxv5X8fYDeBlfi5tXg3cSN0x59XAK/3z8HdrUzL6f0r8WXvZmrJ3CzxQaX8UVexlZG2sjPP/fodDp+KCk83sihbbErvj7kt0/F+98zpK0LV4e9sPL1IfzaTazk3OdzYH4N5yVSy/izwoKI9VZeZ9ZUt6FP8MX8XfxkyRD1t7bB29L1MeXSy+S+uHK8D8Co8xsXxV20PlNiq8D3mHfk4KlyDdwxa3hiwf+g8IUkiU5ucHLraxtOQyva7N219l4fb+YwmLJH+Blz7Z4m0b4O7A3hRHirybZDB8Zn5ry2PB39UP82xiewp2Gv/OLzezilPZN8Xrg/ZRXM/D25lN4WfMUXmZ+D69b36CwBsk6+Pcym5IpNUmBdQCFdtCGFNZ7eQWvP54mWTRI+hJeHhi+g84Zqb31HbxOzSx3v4XXH6Q86ZrL4/y7vSfeFj0Jr2fvz/nLFFtZWxK8ndQZr6u6U6gnoPBOk9IyKed3Cl5uZMqIYRTK1IH495wpR+6ioPwQ3hbOjncws+/m2vfZgGoWdz/8ufTOnl3K5664UrQHcLqZFU3JSgNoP0vhPICXzevg9cftpHoNf1bvJDm7pvzak0LbLxsk3Ab/1s+l0Paem0vz7njbA/x97pXuXyP5kRWmRUwl136UdFMKa7SVmX7VoaGLQdBCbsIbg7fhBcOP8cJyc+AxM7sDL9AX442+23DN2PTc9c8lTZoozPlchCsW3sPNqUemAnIzvLG8M97ZIMV1Gl4QfRFXaBye3NaS9NnUCT41aXLvxBtbH+CFzEJzU9rZZvZrvHPcN/1njc6/4Fr5HXCrjJHm8+hn4YvN7E7BaqMmua2RO+6Gj2C9hxcSz+OF8cKU1u3xDkqflKfrJ/89KVRaj+CKltNTfm2PF4Sb4AXLYZIuT373S3Kvm/J1HbygWgScZmZ1yd8P8Ip0ZJIr7zYW74COxQucz+CNqp54hTZIPpXkNLxx9X6Kox9ekAkv/D6HdzBPSM8oG639frp/RkrHN3Gzwa8mrfpCSbfhBeHupLUJ5Ga//8HXyfhK0sbeild4e+CNWVEwaetlZtfgFeknuAKLJNuuqZH1Ht4wWpTy6tPJz+X4O/tg7h1dB58icW3Kh02T2y65Z7AH3hj7QUr3NyjMU30Ar/AW4o2bOryRDN7IWAdvdPwHbxS9nGT/XLr2d7xxsBbeILgdn6JQl+Ij5VcH0rxrSadI+lbqfHTMPYOLc/KskcLZGTjBzN5Ozysz2e0OTDc3u8w6d1tJ+mlS4t2Hd4w+SHkyzsx+luI6OeVvLYWpCh3xRuI1KQ2d8e8uM7n/BV7xb5/y4Kdmdp25mep/KCy21w3/XubnvsuleKPzk/T7OOf2H3xKxfXp/Klc3nyFYhPIbVPD6Zv4d/41cgvMpmf4DN7h7EDx+3JpCn8U/m3Pwhs8R+Nl1GeT23q5+DPF07R0T9YJWTOl8yS8vL0FwMz+By8X5qW8ewZv+PYkmQWn57UwPY9nU559gH+v+6e8Xzfl80EUGtRz8ZHQLN8mm9nxSY6sHNgSf3fXxcuBffG9yq/O53f675Xy/Hpc2XZdOj4Irxs2xkeOfoYrO/6a0vkJrmT53xT3/hSmSCxN33bWebg/kwFX9ozCy6JRuNVcFuf+ueOdSmRbMz3DTMl7Jf7ezki/55N8WTmeL19uyT8fXKFZi79XmfXNTArTxSbiZdc/KC7D9sjJt2HJ97sxrgAZh5cJA9Iz3SA1Wm/OpftMXIGyLt5gvSW53YTXfQdIOgmvQx/H3+GTcaX1Erws3zq99zvhDdezSObX6d3aMyfb7nhZ+jZebp9MYSHbj0rS0RdXkEwHBqQytB9ejn4TL2tn4N9hNpq6vpndjDemv4MvfnxBKhMyi5kRVpiHPB//TtdM735p/dcZHzC4DlfILUn5NpXib/SXuEVDZ/w7vgu4NXuXceuJ7P35VXrWz+Hl6dlmdkn6BrYCsqkrx+Pvab5NcBv+7XXCp7vchHdQ+uAdyOy570dhcGEWMCu1cz4rn+Z3C94BuQl/3x7D6/GxwMCkXBie3IemZ3BpaZrl5vEn4gMu6+Pl0v8meWbn8nUeaUvAlIcXpd9LeHlk+Gh7ZrX1TJJl45L4JGl3Sd9JI8An4Yrt+4GNVZgyk1m2fJzk+E4u7zpSvP7JUry8yZ7Pt/Bya4v0/1jK79npvmNxJcox+Lfzr/SeZ521kXgdPTCFmdX9c3J1ykL8XV4v3bNx/nvGy8wsX59Jz/g9oFbSxSntm+LvfBe8nPgr3jY+FR+0OQVXwr6Pl8X9szI3vf/d8IWgR+Ht4O6Sfo1/c/l2UI9c3uyCK3ueA76eBncG5mTfVtKRKY+fS/n/jeRvFK6kzKzLSt/thXiZ8PuUjn3xNkfWblyAl33ZoN+hePtqLfxd70vBgtWSPPnv5Tclfj9PKhcp7H5geBv+HFy5daP5YrIj8G/z3HS8fgp7HXzby3z7fgKFds5B5tZkr7PsOiTfxAdy7sf7JQ2RvSMvJrlexq1jr8PL7LXw6X9Zu6cGf0/zbb/7Uv7dS2Fxyo1S3o5KeXEzXn68gL9rNbilW9bu3w9fe+JMXFmyearTdk3hTcafYWYFtAxhwRC0CXKzvT3wD7Cjmd2aczsZH+F/UdK38YJnCf7iXmNmLyZ/38Ff+k7p/1N4AViDa00zc/M9cU3xHIrN0DOzwswUeKr5yOrmOVFPxDufGQeTzPbM7Nb8qC9e+fTElQ5/kk8vyOYKZp0awwu3OcA6VpjCMQDvoDyPF5If4BrIC/HO3HS80slGVMEbIb8qycvaJF9XvKDNGkVH4BVLNjI+AC9EMtPwnXBz83/gnds/4lNM/p3yo4uZTUkjE8dZwazul7jpWOaWjVgZbvVwHgVTtMwSA1zL/6a5qfwByc+/KGiTf0HB+uMyvOB+3MwWSOqCP/Pj8Yr+rCT3InOrkG9SMEX+35z7Vnhl9RzeYZyTc9sVL+yz/DguyVmHN6CfzMl2NN5YupJkYZAlSr42wTfxivGYlBc1eCPkHQpmpl2TW/Y8nsMbV6/hDZh38AZ3Z3MT2MyM7l/ARmb2hKR9zewfko7Jvh8ta7Z3lxXMLLMKU+k5/bUknK+Y2V8kHW9p7mkuXVfiiqrsGeTlOZ7y39bueMf5/1JDby+8cbQPcLeZPZjC2gofsXg3yfcEuWlNkkan+zIzf0vPJWuA1OKN4kfwjmeWBwMomHi/l/zmWQtvUNYlea+nMMLVNcmU3fsG/p0J/zYyk/KsY5OZQG5mZhckt+/iypCMs/EObmZl81m8cTQbbxxk5tX9kqxjaJgvWwMmu5J2STJvacWjk/3wqTC35t4D8IbKX9PxiRSmfQjvaGUjFU/geQkFc/yFFBZkOyi5dUz5tGW6r3vy9zBedk5N71r2fvTAv+290/GiXPo/SL/+Kew98I5DHf7+3IQ/q974N3Y3BRPjrfFy9lK8fO+PvyNL8EbWjvhIy1MpfzZJMuyNv0PZSFNvCtPKjsJHjOpSvv0nJ9t7eJmRf0f/npPnWArzgYUrJEhhzcfL7Oz5XE+hHPiYwjuQvffkzt+jUIbtQWFKz4LUqSal7xYK883zI5OZpdeHufB/gNdnT+J1xFsUrMEG4J3ydXHrlh9bYZrXKSlv70p5PzYn69nA/VawDMxbCX7OzB5Ix1ulvB2X3WfJ7Di5f5nCYoNL0q8H3il9Cu9cdsKVGv/Il2fp3rfxd2VwCnKyuVXZV/Ey/Z2czF3MbLSkY4CXcnXc8VYwhb4zxbkO3gHbIne/8LrixZSvh+HWYufK5yx/ZAVz4zvM7Ih0fF5KV1YuHY2/S2vhbYkj8DJjeorjAHLvXaovsvr9YAoWWqVm7vl2Dvg7+iZeb+yAf7NZOnZLbpslGf6Bdxy64qO++TQ/jZdrW1KYD79Ryp+Xc/n6Q1w5ODoN5nTDn93+eL2Vr5uGU3g/v42vKZHF90OK17M6BW+HvYgrwf5rbqn0KQpTtcDLu2/incyP8G99VgrzHnzdrKxevSZf3qaBguy57onXPe/g38cEoGuuzvsYfx/vBA43s9+mMI5J+Ze1C2vx77kDXtd+JSfvAlwBlOXrPukZZHXepniZ1hFf4PwuSTvgZcInwF/MbKmkP+KKsGxKwgwKOwsJb2tmliLH4d9hZnV3Kz6KPwk4xsxuyqUjs4LIWJKTfRHwsLnV0ykl/o6mMM0Bln23F+b8bpHy5UtmdlZSqGRl+i7Jz1142bMod98xeHk2LbXvS9sSr+T8foFCGQqFMmwPPO/Hp/OOuNLnDQrv9lwKfZVt8YWRb1BaDDHXzhmKl8WfSvJmFssHUVy+74BbshyIWzrcIClTlndIad+AQlvvWQpl4WcoWIMsyB1nbb+X8Ofz2ZR3D1Do1zyU61N8A3/e2fOZS6Gc7JLiXSfl9xb4u94ruT+ND0B0zd6VUkLBEKxUpAZAZ4Cc5rsl4W1oJWZKTbh3I7wCwBpe+bdFSOplZp9U6XcbvDJfks67ZMfpfKilrWuqCGtNKyzsU3/cUJyWVpltjuySZI0UQJL2AJ4p5y/vXvosS9yK8qMtkM99ftjMZqXzVo9T0qfz563xDbQmTZFP0lpmNrucewP+++KV8dPVxpHu65X8VfUtlQljAzN7twp/vfARvHckrQl0M7MPmxHfQDObXsG9o6UFw5oQZsWyrtKzS0q/jKXmo72tjtxySFnnNB9/W32/6TnJfBpR/XFbxNVUGiv/qgyjj5nNaQVZKr0fVddVJWE297769TNWBhqqU1ujXCqJo0nl6YpCUn8ze6+C+zLtipyCsIflts9rJJ5mt+3aC1mdZ2Z/zl0rmy75ArSLqwy7J64MmtWo5+VAUogZrjBaWOLWaLuisTqzivibVRY1EuYw3MLhc/h6Mzfk3HbBFS7v4IM0r5bc2yptvdYIpynvVdF9oWAIgiAIgiAIgiAIgtZB0kbm68L1M7MPyvjZw8yebshtZSYUDEEQBEEQBEEQBEEQtJhY5DEIgiAIgiAIgiAIghYTCoagTcgv9lK68Eu1bq0VzvKIY3WUtbXCCVnbdzgha8jaWuGErCFra4UTsrbvcELWkLW1wglZ27esZTGz+MWv1X/4LhHLHDfFrbXCWR5xrI6yro5pXplkXR3THLK273BC1pB1dUzzyiTr6pjmkLV9hxOytm9Zy/3CgiEIgiAIgiAIgiAIghYTizwGrUoXdbVu9KCGxXROW7Pmj4vOJWpsEZ3VDdJ7mLnVru1b9y5dNJ9O3XrQcdaCer/9tlkAwMezaundtyMfTF2jEE4WRzqvXXsNli6cT6fuPep3oM3CVNporWbxPDp37UnHuYtZUreQLh26Y0uXFsljvdagZsl8OnfpgXVQ8X2f+I46S2wRXdQNq6srn+ZK+VHBTR1dF7ikbhFdOnTDaquLQx06LCNbs+RZw/O2ZukCOndaAxYsapV0NeZWdF7hfSlyy+4r43fz7Rfw4cxa1l27I69P6VmcP2bl46j4fDoWh1NbWyRbqTxVp7m18q6dxxGyrh7hhKwh6+qY5pVJ1tUxzSFr+w4nZG2/ss5l9kdmti4N0Kmhi0HQXLrRg120X+FCh46F47ribdvVtfCy2uLiLVbnHLJb0flaf55Yf3zG34u3v/7dVtuXlWfmYYMLoiwtdus8v3gr915PvVl/XPvBjCK3xXvuXHDrVmz40/ORl4vO6+bOLStPc+nYs1fRee0n1W3X22GNHkXndfPnNyt+bb1N0blNnFrGZ9uhzl0K8S+tKXbr1LnsfVazpOj8wQcn1R9/fothxX4XFd7D0vsq0bH3WkXntbNnF2TLyQ0lsoeCNwiCIAiCIFjJeMRGv1XObbWdIiFpXhuEOUjSQbnzCyWd25oySTpT0iuSbpH0RUlb59xGSTqyqWFWKduhkka0RlhBEARBEARBEATBqsdqq2BoIwYBBzXmqYWcDhxkZscCXwS2ruy9dTCzMWZ22fKIKwiCIAiCIAiCIFj5CAUDIOk8SeMlvSTponRtYLIUuEHSVEkPSeqe3HZOfsdJulzSFEldgIuBoyVNknR0Cn5rSWMlTZN0Zgtlug74DDBG0o+BQ4HLU3ybtCCdr0q6MaXjFkn7S3pa0uuShiZ/J0i6pky4p0iaIGlCDYsb8hIEQRAEQRAEQRCs4qz2CgZJw4HNgKG4BcJgSXsl582A35vZNsAc4Ih0/SbgVDPbDagFMLMlwPnAbWY2yMxuS363BA5M4V8gqfxk8UZkMrNTgXeBfczsF8AY4LwU3xvp9kzhMEnSpCrTuSlwFbB9kvcYYE/gXOBHjclrZiPNbIiZDckvAhIEQRAEQRAEQRCsPsQijzA8/f6ZznviHfG3gTfNbFK6PhEYKKkPsKaZPZOu3wocUiH8e81sMbBY0gygH/DfZsr0RBXpOc/MRmcnuTUYGkvn5OR/KvComZmkycDAKuIMgiAIgiAIgiAIVnNCwQACLjWz64suSgOhyN6/Fuie/DeF0jCqyfMGZWoh1aazLndeR7wjQRAEQRAEQRAEQRVE5xEeBH4m6RYzmydpAFBTzrOZzZY0V9KuZvYs8JWc81xgzbaSycxmlPhrSnxNSmeLUE4HY3Xl/dWV36Jvk2+9WnQ++4XP1B+f9Xzxupab1EwqG856900riFJTnFybv6D4fIuN6487de9W5NbppXcKJ52LP5vaRSXrThSlv5W2IexSYWZNfitQoEO3wjSVuoWLWif+F//VOuE0gdLtHTuut079cek2oh3XLmwTWTfn4yI3K3nLN777m/XHW/WfVRzn7ML2n7Uffli9sP1LtgHObVNZut2lOhXeH1tasndqEARBEARBEKzErPYKBjN7SNJWwDh5x3AecBxpbYUynAzcIGk+MBbIejSPASPS2geXtoFMpQqGvyQ5zgSOpALNTGcQBEEQBEEQBEEQVMVqq2Aws56546vwRQ5L2Tbn54rc9almtj2ApBHAhORnFrBzhTi3LedWrUxmNjB3/DTF21Se0JwwKU7nCbnj6ZmbmY0CRlWSPwiCIAiCIAiCIFh9WaV3kcgtcNiaYQ4Cfpx2aZgCnATMqnxXxfDuSwtHVvIzVtKQhmSRdFDufJmtJMvd2ww5N5A0unGfQRAEQRAEQRAEwerIKq1gaCMG4btIDEoWCbcC86u9WdLaJdtIbgCMlbR2M2U5qDFPrYGZvWtmFadhBEEQBEEQBEEQBKsvq42CQdJ5ksZLeknSRenaQEmvSLpB0lRJD0nqntx2Tn7HSbpc0hRJXYCLgaOTguDoFPzWyVJgWloPoZwMA4GngGcAAw4D+gD7m9lMST+V9KqkhyX9WdK5uduPkvS8pNckDasgS6U8GJ7S84Kk2yX1TNenS7okuU2QtJOkByW9IenUXF5NaUqeB0EQBEEQBEEQBKsPq4WCQdJwYDNgKD7qP1jSXsl5M+D3ZrYNMAc4Il2/CTjVzHYjLYRoZkuA84HbkgXDbcnvlsCBKfwLJFVY8p8tgD+Y2Y5m9lZOxiEp7h2BLwGl0xo6mdlQ4GzgggqyZAqHzEJiSAp/HeAnuDJjJ3zdiO/mwv9PSuuT+FoLRwK74kqMikg6JSkmJtSwuDHvQRAEQRAEQRAEwSrI6rLI4/D0+2c674krFt4G3jSzSen6RGBgWhNhTTN7Jl2/FTikQvj3mtliYLGkGUA/4L9l/L6VtrcsZU/g72a2EEDS3SXuf8vLWEGW28zsO9mJpLHpcFd8Qcin0y4SXYBxufvGpP/JQE8zmwvMlbSosTUizGwkMBKgl/q20r6MQRAEQRAEQRAEwcrE6qJgEHCpmV1fdNGnLOSH3GuB7sl/UygNo1K+lluvobE4szgaC78cAh42s682En4dxempa2Z8QRAEQRAEQRAEwWrEajFFAngQOCm35sAASeuV82xms/HR+13Tpa/knOcCa7aBjE8BX5DULcl5cBX3NEWWZ4E9JG0KIGkNSZs3T9QgCIIgCIIgCIIgKGa1GJk2s4ckbQWMS9MD5gHHkdZWKMPJwA2S5gNjgY/T9ceAEWl9g0tbUcbxksYALwJv4WskfFz5ruplMbMPJZ0A/FlS13T5J8BrLZG7TGSF4w4dc9dLsrtDeaONt35brPtY81//rD/u1XPjIjd17lI2nJqN168/7jTtveL7Nuxf7Pn9mfWHSz+YUeS05MDCkhgdaopngXR+5/3icKz1Z4nUza2w42pdcb7WLVjQ6vF3GPipovPaf7/Z6nGUYjVLiuOc8VHBrbY4zbUzZ1cd7ptfuKH++PPnDiuOc1Ez1xB578OyTqXvpy2taV4cQRAEQRAEQdDOWaUVDGbWM3d8FXBVA962zfm5Ind9qpltDyBpBN7hx8xmATtXiHPbCm7T8/GlawNzp1eY2YWS1gCeAH6d/Oyd8/8RaQ2GMrKMKgk/f+8/GpI9L4OZjcqHkXP7qFT2IAiCIAiCIAiCIMho91MkJFUYum12mIMkHZQ7v7BkS0iAg9NODFOAYcDPc/6foRlIuljS/hW8vCHpX8ALwB1m9kIDYQxLW2pOkrRbSTpOkHRNif+xaYeKFiFpA0mjWxpOEARBEARBEARBsGqySlswVGAQvn3jfeU8pG0fbyvjtnulwCWtDTzagNN+ZjazgesZrwDnmtmECn6OxS0dbkpTHiqmo7Uws3fxrSuDIAiCIAiCIAiCYBnavQVDHknnSRov6SVJF6VrAyW9IumGNLL/kKTuyW3n5HecpMslTZHUBbgYODpZARydgt86jfZPk3RmI3LMS/97S3pC0p2SXpZ0naQOwBxgEq7A6QjcbGaDgF9LqqqTLml4kvsFSbdL6inpG8CXgfMl/blMOpoUZro+XdIlyW2CpJ0kPSjpDUmn5vJ5SjWyB0EQBEEQBEEQBKsfK42CQdJwYDNgKG6BMFjSXsl5M+D3ZrYN3rk/Il2/CTjVzHYjLehoZkuA84HbzGxQslQA2BI4MIV/gaTOVYo2FPgesB2wCfClJN8AM9vWzLZLcjQlrevgCzDub2Y74es/fNfMbgTGAOel7SYbSkemcJiUFn8cUinMXLT/Sfn0JL4Gw5HArrgSozF5T0mKiQk1NHORvCAIgiAIgiAIgmClZmWaIjE8/bLtBHriioW3gTfNbFK6PhEYKKkPsKaZZesl3AocUiH8e81sMbBY0gygH/DfKuR63symASSrgj3x6RGfkfQ74F7goapSWGBXYGvg6bTrRRdgXJX33mZm38lOJI2tMswx6X8y0NPM5uJbdS5KeVkWMxsJjATopb6tv4VCEARBEARBEARB0O5ZmRQMAi41s+uLLkoDoWjYvBbonvw3hdIwqs2b0g61mdlsSTvgFhHfxqc1nNQEWQQ8nKwUWovGwszSX0dxXtSxcr0nQRAEQRAEQRAEwQpgpZkiATwInJRbN2CApPXKeTaz2fgI/K7p0ldyznOBNVtJrqGSNk5rLxwNPJWmI3QwszuAnwI7NTHMZ4E9JG0KIGkNSZs34K8p6ag2zCAIgiAIgiAIgiBoMivNyLSZPSRpK2BcMvGfBxxHWluhDCcDN0iaD4wFPk7XHwNGpDUKLm2haOOAy/A1GJ4A7kzHNyWlA8APmxKgmX2Ydoj4s6Su6fJPgNdKvFadjiaE2XKUMx6pq/B4asu7vTu8ruh8q5c+XX88e0axTmXdmiVlw+n06tv1x7Z0aZGbzZpTdN5xw/6F+/qvX+w2qRCOOhcvz1HpBWwtOnTvVhzn4pyRSYeOxX67da0/rltUsiZGpedRgbrp/2nWfS1BnYqLpw59+9Qf135YvBlLhz69649t7twiN6spDnfI+afVH6/X7d/Fjrl30iq8V8vIulbv4guzZ5cPR001rgqCIAiCIAiClYN2r2Aws56546uAqxrwtm3OzxW561PNbHsASSPwhQ0xs1nAzhXi3LacW6lMwAIzK93B4UUasFowsxMaCXfv3PE/GpIxH0aZdIxqRpgDc8ej8mHk3D4il89BEARBEARBEARBkKfdKxhayMGSfoin8y3ghBUrThAEQRAEQRAEQRCsmrSLNRgkzWuDMAcBc9MWjtsC44GvN+H+5/PbPeZ+a2d+zGysmR1Sct/FkvZvJOw7c+G9L+nfkg6s4P9CSec2cL2PpNNz5wMlTanm3uYg6b7GdpQIgiAIgiAIgiAIVk9WZQuGQcAQ4L7m3GxmQ5t53/lV+DkcQFJHM2vJFP4+wOnAtS0Io2rM7KDlEU8QBEEQBEEQBEGw8tEuLBjySDpP0nhJL0m6KF0bKOkVSTdImirpIUndk9vOye84SZdLmiKpC3AxcHSyEsjWSNha0lhJ0ySd2Ygc89L/3pKeSFYHL0u6TlIHSR0ljUrxTZZ0TvI/StKRFcKdLul8SU8BR+X9SzpI0quSnpJ0taR7crc2JPtlwCYpjZdXkbebSHpA0kRJT0raMifz/0h6LIX/WUn/l/J8VIns6zQQ7imSJkiaUMPiUucgCIIgCIIgCIJgNaBdKRgkDQc2A4biFgiDJe2VnDcDfm9m2wBzgCPS9ZuAU81sN9KC/ma2BDgfuC1Nkbgt+d0SODCFf4Gk4u0AyjMU+B6+O8QmwJeSfAPMbFsz2y7JUS2LzGxPM/tLLu3dgOuBz5vZnsC6Jfc0JPsI4I2UxvOSv0zhMCntLnFqLoyRwBlmNhg4l2LLh7WAfYFzgLuBK4FtgO3SdJOymNlIMxtiZkM607WS1yAIgiAIgiAIgmAVpV0pGIDh6fdP4AW8U71ZcnvTzCal44nAwLQewJpm9ky6fmsj4d9rZovN7CNgBtCvSrmeN7NpaTrDn4E9gWnAZyT9TtLngE+qDAvgtgaubQlMM7M30/mfmyl7pnAYZGaDgOsAJPUEdgduT4qH64H+ufvuNjMDJgMfmNlkM6sDpgIDm5C2IAiCIAiCIAiCYDWkva3BIOBSM7u+6KI0EIps72uB7sl/UygNo9r0W+m5mc2WtANuVfBt4MvASVWGN7+Ba42lpbmyZ3QA5iSlQ6Xw60riqmtGXEEQBEEQBEEQBMFqRnvrOD4I/EzSLWY2T9IAoKac59TJnytpVzN7FvhKznkusGYryTVU0sb4VpdHAyPTWgRLzOwOSW8Ao1oYx6u4RcRAM5ue4mmMqtNoZp9IelPSUWZ2uyQB25vZi80XuWxk1XlburSs25Znv1x0Xju/oJP59F3Vr79Z95kN6o8XbtCjyK37ewuKzpdOKNqAoxgV9D8d+65V7NahqXquplM3f2EFx+J1QusWFNLVoUdxmuvmN6TbapwOaxa/ZrWzZzcrnKZQ+n7UfjCjrN/aDz+sOty1bxxXuK/pYjXI0mnTq/dc5fcRBEEQBEEQBCsb7WqKhJk9hE9zGCdpMjCaxjvQJ+Md/nG4FcDH6fpj+MKI+UUem8s4fEHFKcCbwJ3AAGBsmm4wCvhhSyIws4X4jhAPpAUgP6CQlnL3zASeTgtNNrrII3AscLKkF/GpD4e1ROYgCIIgCIIgCIIgyJCt5KNpknqaWbbjwwigv5md1Yrh7w2ca2aHtFaYFeLqmSw3BPweeN3MrmzreFuTXupru2i/FodTaeR90SHFFgzd7nm+bDgavE39cWMWDNZMC4a6ecVWAba49XfSUOcuxXHULKnqvtayYOi4VnGal4cFQxAEQRAEQRAE7Y9HbPREMxvSkNtysWDItnxs5TAHSToIODhZKcwAvgb8vLXjqiDDQEnHtGKQM5NFxFSgN74QY2mcZ6btI2+R9EVJW+fcltkis7XyXtKhSYETBEEQBEEQBEEQBMvQ3tZgaAqDgCFm9h3gNkkXAvPMrOrJ2JLWBh5twGm/NP0AMxsLjC0TxEDgGBrYvULSXSy7+8IPzOzBCiLVVFiEMeN0fCvLNyWNAu4BXq58S8sxszHAmLaOJwiCIAiCIAiCIFg5We5rMEg6T9J4SS9JuihdG5hG5W+QNFXSQ5K6J7edk99xki5P6w10AS4Gji5ZY2FrSWMlTZN0ZiNyfA1fp0HA5NSxPwyYCTwm6VFJn05+R0m6WtIzKezMSuAyYFiS4RxJJ0i6XdLdQC98O8uZ+E4MHYFuLcyn64DPAGMk/Rg4FLg8xb9JM8McKOlVSTemvL1F0v6Snpb0uqShyd8Jkq6pVv4gCIIgCIIgCIJg9WK5KhgkDQc2A4biFgiDJe2VnDcDfm9m2wBzgCPS9ZuAU81sN9Ki72a2BDgfuM3MBpnZbcnvlvi2kUOBCyR1LiPHNsCPgX3NbAcgW7PhGuAPZrY9cAtwde62/rjC4BBcsQAwAngyyZCtlbAb8HUz2xdYBBxuZjsB+wC/TusrNCufzOxU4F1gHzP7BW5RcF6K/410e6ZwmJSmW1QMMzlvClwFbJ/y8JiU1nOBH1Uh7ymSJkiaUEPrrz8QBEEQBEEQBEEQtH+W9xSJ4en3z3TeE+/0vg28aWaT0vWJwEBJfYA1zeyZdP1WvINfjnvNbDGwOK3J0A/4bwP+9gVGm9lHAGY2K13fDfhSOv4j8KvcPXeZWR3wsqR+FWR4OBeegEtSR74O33miH/B+hfuhfD490ch94AqH0dlJbg2GxvJ+cvI/FXjUzCzt5DGwsQjNbCQwEnyRxypkDIIgCIIgCIIgCFYxlreCQcClZla0eKGkgVA09F0LdE/+m0JpGOXSJ6CajnDeTz7sSnLll+k/FlgXGGxmNZKmU900iQbzqYVUm/d1ufM6Vu51OoIgCIIgCIIgCILlxPJeg+FB4CRJPQEkDZC0XjnPZjYbmCtp13TpKznnucCazZTjUeDLaZFHJPVN15/JxXEs8FQj4TQmQ29gRlIu7ANsVKV81eZTU/KgSXkfBEEQBEEQBEEQBE1huY5Om9lDkrYCxqWlCOYBx5HWVijDycANkubjuzl8nK4/BoxI6wxc2kQ5pkr6BfC4pFp82sAJwJnA/0k6D/gQOLGRoF4Clkp6ERgFzC5xvwW4W9IEYBLwapXylcunGSVe/4LnzZnAkVSgmXm/wlg6ZPOi845PT64/XuPxV4rc6ioF9NLr9YfdXyp2sqU1xXH06V0Ic+GiYs+1hWyq+/iTknCWFvvNL7NhrTRjxCqmsix18+c37qkKameXvtrLH3UqFFfL5HmHjoXjusqv9JIDC1v2dp8wrcjNcs+9bsGCqmXr0K3YMKlu0aIyPmmb9yMIgiAIgiAI2gGydt7AldTTzOal4xFAfzM7q5HbghVEL/W1XbRfi8Op++yORed5BYO6dy/2O3du2XDUuUtZt2UUDL17FcKsoGBYNpy2VzDkO9cNxrkaEAqGIAiCIAiCIFjxPGKjJ5rZkIbcWm2KRG4xwVZD0iDgx2lHhCnAScCsyne1ugwDJR3ThuHflxazrORnrKRlHqCkQZIOyp0vs5VkuXubIecGkkY37jMIgiAIgiAIgiBYHWnvC/gNwneRGAQg6UKKF1GsSFpj4dEGnPYzs5lVBjMQ37bx1gbC72RmTRpKLiPTWElNkSljEDAEuK+J9zUZM3uXRqZhBEEQBEEQBEEQBKsvbbLIo6TzJI2X9JKki9K1gZJekXSDpKmSHpLUPbntnPyOk3S5pCmSugAXA0cnC4ajU/Bbp1H5aWntgUocnNIoYHJSVBwG/DXF96ikTycZRkm6WtIzKeysM30ZMCzJcE6yErhd0t3AQ5J6pnBekDRZ0mEV8mUgvnDkM/gOFYcBfYD9zWympJ9KelXSw5L+LOnc3O1HSXpe0muShlXIn0rPZXjK4xdSGrIFH6dLuiS5TZC0k6QHJb0h6dTc85vSWBxBEARBEARBEATB6kmrKxgkDQc2A4biI+yDJe2VnDcDfm9m2wBzgCPS9ZuAU81sN9Kig2a2BDgfuM3MBpnZbcnvlsCBKfwLJHUuI8c2wI+Bfc1sByBbt+Ea4A9mtj2+COPVudv6A3sCh+CKBYARwJNJhivTtd2Ar5vZvsAi4HAz2wnYB/i1pErbWG6R4t/RzN7KyTsk5ceOwJdwy4Q8ncxsKHA2cEGF/MkUDpPSAphDUvjrAD/BlRk7AROA7+bC/0/K/yfxBSuPBHbFlRgVkXRKUkxMqCna8TIIgiAIgiAIgiBYXWiLKRLD0++f6bwnrlh4G3jTzCal6xOBgWn9gTXN7Jl0/Va8g1+Oe81sMbBY0gygH/DfBvztC4w2s48AzCxbu2E3vAMP8EfgV7l77jKzOuBlSf0qyPBwLjwBlyQlSh0wIMn0fpl73zKzZxu4vifwdzNbCJAsJPL8Lf1PxKdtlOM2M/tOdiJpbDrcFdgaeDrpP7oA43L3jUn/k4GeZjYX3yJ0UWNrRJjZSGAk+CKPlfwGQRAEQRAEQRAEqyZtoWAQcKmZXV900acH5Ie3a4HuyX9TKA2jXBqET0NojLyffNiV5MqvA3EssC4w2MxqJE0HujV417L35mksHzLZKqW5EsIVI19tJPw6ivOhrpnxBUEQBEEQBEEQBKsRbbEGw4PASbn5/QMkrVfOs5nNxkfKd02XvpJzngus2Uw5HgW+nBZVRFLfdP2ZXBzH4msiVKIxGXoDM5JyYR9go2bK+xTwBUndUt4dXMU9TcmfZ4E9JG0KIGkNSZs3T9QgCIIgCIIgCIIgKKbVR6bN7CFJWwHjkin+POA40toKZTgZuEHSfGAs8HG6/hgwIq0lcGkT5Zgq6RfA45Jq8SkbJwBnAv8n6TzgQ+DERoJ6CVgq6UV8bYLZJe63AHdLmgBMAl5tipw5ecdLGgO8CLyFr5HwceW7qs8fM/tQ0gnAnyV1TZd/ArzWHHnbmi5vF+9GunRpYbOObvesUeS28LNzCycly1/YjlvUH3ecu6jYrXPHovO6f71ZcFtcvJZEx602qz+uXbPYQEUvFmdh6b2tgdU1b+aJunYtOm+ubJ36r190vvS9cjOA2g6rzRUhpcucWF3V4SxYr7BsS7f5C4rc6pr77DqXLAWzKPeuLSNrzCIKgiAIgiAIVk1k7aCxK6mnmc1LxyOA/mZ2ViO3rXJk+SBpDeAJ4BQze2FFy9UUeqmv7aL9WhxOp42LDUGWvlm/HibdHy9eHmPhZz8onJR25nbetv6wMQWDtWMFAx2KZaWukr6uwKqkYFjm2ZajkTJtzvG71R/3vf2fRW5FCoYmlI0d1iw2JKqbW17pFQqGIAiCIAiCYGXmERs90cxKNyUA2mibymZwcNr1YAowDPh5cwOS1EfS6a0nWlHYhyYFSHPvn1fBbSDw32SN8AJwR0PKhbSN59T0f4KkDXJuY9NuFPVhttbWkpJOlfS11ggrCIIgCIIgCIIgWPVoF4v3pS0Wb2vUYwOkNRYezV3qAmws6TYzm1nF/cItORq1sTazMRR2W2iKTNXyXzMb1IifbwHrmtnitEPEFODdZsTVJMzsuraOIwiCIAiCIAiCIFh5aS8WDM3GzGaa2aDsh6+ZAPBoGuU/T9J4SS9JugjqR/ZfkXQtbi0wTNKrkm6UNEXSLZL2l/S0pNclDU33nSDpmnQ8StLVkp6RNE3Skel6T+Cv+O4LHYELcrJVhaSOSfZM7m+l62OAHsBzko4GhgC3JOuP7s0Mc29Jj0v6q6TXJF0m6VhJz0uaLGmT5O9CSedWm4YgCIIgCIIgCIJg9aJdWDC0MiOAbc1skKThwJHAUHybxjGS9gLeBrYATjSz09P0hE2Bo4BTgPHAMcCewKHAj4AvNhBX/+RnS9yyYTSwCDjczD6RtA7wrKQx1rTFLk4GPjazndOCjE9LesjMDpU0L1NWSDoNONfMJqRzcIXDwhROF1zRUTbM5LYDsBUwC5gG3GhmQyWdBZwBnF1JWEmn4PlGN9ao5DUIgiAIgiAIgiBYRVkVFQx5hqdftpJbT2AzXMHwlpk9m/P7pplNBpA0FXjUzEzSZGBgmfDvSlMrXpaUrTwo4JKkyKgDBgD9gKasijcc2D6zisC3wtwMeLP8LfUcm1M4DATuaSTMJcB4M3sv3fMGkCkeJgP7NBahmY0ERoIv8liFjEEQBEEQBEEQBMEqxqquYBBwqZldX3TRO97zS/zml9evy53XUT6f8vdkS8UfC6wLDDazGknTgW6lN1Yh9xlm9mAT72tymJL2pnlpD4IgCIIgCIIgCIJ6Vvo1GBpgLpDtGfcgcFJaFwFJAySt18bx9wZmJOXCPsBGjd3QAA8Cp0nqDCBpc0k9GvCXT2trhRkEQRAEQRAEQRAETWaVG502s5lpccYpwP3ArcC4tD7BPOA4oLYNRbgFuFvSBGAS8GozwrgRn5bxQtrl4kMaXgNiFHBdWnNht1YKs13w9pEDis4/NWpB/fGrHxTriDbig8JJyVIXHV97u+C0aHGRW92SmmK/PQv6FutU/GnYW+8U/HXtWhxObVu+Tk6HbiVxLliQc+xY9j5bsqRV4l/6wYetEk5LUKfO9ce2tPjZdejZs+BW8pxL/a7x4dJCmD2K1wxRbWEzGaupPu+0Rskaq3Pn5gQomTUklXcLgiAIgiAIgpUYNW3twSCoTC/1tV20X4vDefe83YvOPzXq9frjab9fv8htoy9PLhtOxz6964+bpGAoVRrkvhOVKhjynUnAli6ltemwRnFHuFoFA6W7rzb3ey+No67tlSqlqHOX+uOWKBiWHDik/rj7+DeK3Oo+mVe4rwkKho79ipVetR/MKO85FAxBEARBEATBSswjNnqimQ1pyG1VnCIRBEEQBEEQBEEQBMFyZqVTMEjqI+n0Ngr7UEkjWnD/vCr8bCdpUvrNT//PVfA/PW13WXp9b0m7584vlHRuNfc2FUlDJF3d0nCCIAiCIAiCIAiCVZeVcQ2GPsDpwLXVeE7rDShtJ1kRMxsDjGmRdI3HMVnSYDNrqY353viaEs+0XKrKpG0vJ7R1PEEQBEEQBEEQBMHKy0pnwQBcBmySRv4vl3SepPGSXpJ0Efg2lJJekXQt8AIwTNKrkm6UNEXSLZL2T4tBvi5paLrvBEnXpONRkq6W9IykaZKOTNd7SnpU0guSJks6rBqhk8XBY5JuBSana/PSfwdJ10qaKukeSfdl8SXOyMW3Zdpm81TgnJQPw6qI/zhJzyf/10vqmMkg6ZeSJkp6RNJQSWNTmg/NyX5PhbBPkTRB0oQaFpfzFgRBEARBEARBEKzCrIwKhhHAG2Y2CHgY2AwYCgwCBkvaK/nbAviDme0IvAVsClwFbA9sCRwD7AmcC/yoTFz9k59DcMUGwCLgcDPbCdgH+HWykqiGocCPzWzrkutfwnd42A74BsvuCPFRiu9/gHPNbDpwHXClmQ0ysyeTv0zhMEnSJGADAElbAUcDe6R8qwWOTff0AMaa2WB828ufAwcAhwMXV5MoMxtpZkPMbEhnujZ+QxAEQRAEQRAEQbDKsTJOkcgzPP3+mc574gqHt4G3zOzZnN83zSyzHJgKPGpmJmky3rlviLvS1IqXJfVL1wRckhQZdcAAoB/wfhXyPm9mbzZwfU/g9hTX+5IeK3H/W/qfiCsjynGlmV2RnUiang73AwYD45MupDuQLXO/BHggHU8GFptZTSP5EgRBEARBEARBEARFrOwKBgGXmtn1RRd9CsH8Er952/263Hkd5fMhf09mpXAssC4wOHXEpwPdqpS3VKbSsMuRyVFL856ZgJvN7IcNuNVYYa/S+nwxszpJK/v7EQRBEARBEARBECwnVsYpEnOBNdPxg8BJknoCSBogab2yd7YOvYEZSbmwD7BRK4T5FHBEWouhH76AY2Pk86ExHgWOzPJGUl9JrSF3EARBEARBEARBEAAroQWDmc1MizNOAe4HbgXGJdP/ecBx+Eh/W3ELcLekCcAk4NVWCPMOfBrDFOA14Dng40buuRsYnRaZPKOSRzN7WdJPgIckdQBqgG/ja1O0Sz79l7eLzpd++GH9cYcO/Uq9FyhZDkM9e9Yf25KaIreOvXoWndctXFTwu7h4scqOm29ScOvepTjOl8sZprQedQsXVnBsy9fd6di7V9F57ezZbR5nKba0pqxb3bzcDrH1BjkNs2DdQrHXbf6CquOoKNuCCs+ndImWRuQLgiAIgiAIgpUVWTR22wWSeprZPElrA8/jCzJWs65Du6KX+tou2q/F4XT61IZF50v/89/64/+M3rbI7VNHTimclHTmOg3YoP64blZxp1hdOhedN1fBYC+/UXxes4RWZwV3UjuutVbR+YpQMCyTB+VoJG/mHF9YQ7Xv7f8scqvLP/cm5HGHNYuNiermzi2chIIhCIIgCIIgWIV4xEZPNLMhDbmtkCkSkvpIOr2Nwj5U0ogW3D+vgtsGkkY3M9wbJZXuHpHno7Sw4pPAzxpSLkg6Km2/+VjaOnL3nNuFks4t8T9d0jrNkbcknCGSrm5pOEEQBEEQBEEQBMGqy4qaItEHOB24thrPaRtIpV0WKmJmY4AxLZKufNjvAkdW8iNpO+CPJZcXm9kujQT/PrCPmX1Uwc/JwOlm9pikC/EpIc80Em6LMbMJwIS2jicIgiAIgiAIgiBYeVlRizxeBmwiaZKkyyWdJ2m8pJckXQS+E0Qarb8WeAEYJunVZAkwRdItkvZP6zG8Lmlouu8ESdek41GSrpb0jKRpko5M13tKelTSC5Imp3UMGiXJNCUXz98lPSDpX5IuSN6mAe/gOzd0wne52EXSWEkNmpE0EM9xkp5P+XO9pI6Szse3s7xO0u3AqcA5yc+w5oSZrs+T9EtJEyU9ImloknWapEOTn70l3VON7EEQBEEQBEEQBMHqyYpSMIwA3jCzQcDDwGbAUGAQMFjSXsnfFsAfzGxHfEHCTYGrgO2BLYFj8E73ucCPysTVP/k5BFdsACwCDjeznYB9gF8nK4mmMhTftnIQcFRSIHwOeNfMdjCzbYEHmhKgpK2Ao/E1GAbhC1Yea2YX41YEx5rZUcB1wJVmNsjMnky3ZwqHSZImARtUCjPd0wMYa2aD8Z0pfg4cABwOXFylzKdImiBpQg2LG78hCIIgCIIgCIIgWOVoD7tIDE+/bLW1nrjC4W3gLTN7Nuf3TTObDCBpKvComVlau2BgmfDvSlMrXk5bQIJbF1ySFBl1wACgHz5NoSk8bGYzkzx/wxUZ9wFXSPolcE+u818t+wGDgfFJ59EdmFHlvVea2RXZiaTpVYS5hIISZDI+naOmkTwtwsxGAiPBF3msUtYgCIIgCIIgCIJgFaI9KBiETyO4vuiiNBAo3f8vPzxelzuvo3xa8vdkVgrHAusCg1NnejrQrcmSQ2ln2szsNUmDgYOASyU9lKwPqkXAzWb2w2bI05wwa6ywlUh9nppZnaT28H4EQRAEQRAEQRAEKwEraorEXCDb1+1B4CRJPQEkDZC0XhvH3xuYkZQL+wAbNTOcAyT1ldQd+CLwtKQNgAVm9ifgCmCnJob5KHBklgcp/Ibky+dha4UZBEEQBEEQBEEQBM1ihYxQm9nMtDjjFOB+4FZgXDLfnwcch68T0FbcAtwtaQIwCXi1meE8he8YsSlwq5lNkHQgcLmkOqAGOK0pAZrZy5J+AjwkqUMK49v4GhR57gZGpwUqz2ilMNsNMw74VNH5umMW1h8vrelY/kYrNiqxT+YWjmuWFrnVLSpeL6JDt671x+pWbNBib7+Tc+ta7Nb45iYtRl26FMe5OCd7h0r5USKbNW8GS+2cOc26rzVRp871x7a0psitQ8+eBbeS51rqd+2JM+uP60rzQzmdq1VfBKlL5/KOzczzIAiCIAiCIFjZkEXjt1lIOgEYYmbfWdGytCd6qa/tov1aHM6sk3YrOl93zGv1x//+3YZFbht/9cWy4XTs1av+uG7hoiI3qy3uQOYVDNSVVxqUKhjq5hXP5LGlxYqM1kBdS5Qay1nBQOkaqCug3FDngpKlJQqGjlttVn9c90axjq1ICVVXvYKh49p9i85rZ86q+t4gCIIgCIIgWJl4xEZPNLMGd0hssykSkvpIOr2Nwj5U0ogW3D+vgtsGkkY3M9wbJW3dmv7TVptHNnB9oKRjcufLbCVZ7t7mIOmZ1ggnCIIgCIIgCIIgWDVpyykSfYDTgWur8Zy2iZRVYW9uZmOAMS2SrnzY70q6KG3zmGexme2S8zcKGFVy7zcaC1/Sc0B+OPpWScdnu2M0gYH4Np23NvG+ZmFmuy+PeIIgCIIgCIIgCIKVk7Zc5PEyYBNJkyRdLuk8SeMlvSTpIqgfhX9F0rXAC8AwSa+mkf0pkm6RtH9ar+F1SUPTfSdIuiYdj5J0taRnJE3LRuwl9ZT0qKQXJE1OaxU0Stq94s9mNgj4Lb5OwftAH0kXJD89JN0r6cUk59Hp+lhJDZqKJPd5+KKWi4HvAHOAb5jZZEknS3othXFDlr7EXqXpS/k7LOXvOVWka7CkxyVNlPSgpP45ma+U9ER6FjtL+lvK75+XyB4EQRAEQRAEQRAEDdKWFgwjgG3NbJCk4cCRwFB8y8QxkvYC3ga2AE40s9NT535T4CjgFGA8Pkq/J3Ao8CN8t4ZS+ic/W+KWDaOBRcDhZvaJpHWAZyWNsaYvOjEU2BZYAIyXdC++68S7ZnYwgKTeVYbVA5hiZuen+0j/GwA/xXecmAv8A8gvLNBQ+kYA55rZISmMvUkKh9x9nwbukdQZ+B1wmJl9mBQivwBOSv6WmNleks4C/g4MBmYBb0i60sxmUgFJp+DPi26sUWVWBEEQBEEQBEEQBKsSy2sXieHp98903hPYDFcwvGVmz+b8vplNF5A0FXjUzEzSZHxaQEPclaZWvCypX7om4JKkyKgDBgD9cGuEpvBw1sGW9De8o38fcIWkXwL3mNmTVYZVC9zRwPWhwONmNivFczuwec69ofQ1xJOZwiGFMyodboErSR5OSo2OwHu5+7LpJpOBqWb2Xrp/GvApoKKCwcxGAiPBF3ms5DcIgiAIgiAIgiBYNVleCgYBl5rZ9UUX3WJhfonf/BLwdbnzOsrLm78nW+7+WGBdYLCZ1UiaDnQrvbEKSjvMZmavSRoMHARcKukhM7u4irAWmTW4950auJanofQ1BeGKg93KuOfzuDT/V8hWpkEQBEEQBEEQBMHKRVuuwTAXWDMdPwicJKkngKQBktZrw7gBegMzknJhH3xaQ3M4QFJfSd3x6RlPpykNC8zsT8AV+NSGlvA88FlJa0nqBBxRxT35/G2MfwHrStoNQFJnSds0T9QgCIIgCIIgCIIgWJY2G502s5lpccYpwP34bgfjkon+POA4fMpAW3ELcLekCcAk4NVmhvMU8Ed8bYhbzWyCpAOByyXVATXAaS0R1MzekXQJ8BzwLvAy8HEjt70ELJX0Ir6bxT/LeTSzJWlxyKvTehGd8AUsp7ZE7rak76jni85r6wqvymY/6lnktrRCOOpV0MF07NunYpxLp79dIaCC4YhKlvGw2rZ8jVMcixeXd6wrH7+6di06rxhOBdSlS6uE0xKsZklZt7q5c6sOp/bl11pDnOIwZ85q9TCDIAiCIAiCYGVDTV/zcPVB0gnAEDP7znKIq6eZzUsWDHcC/2dmd7Z1vK1NL/W1XbRfywPq0LH4PNeJ7rRxsTHK0jffKhtMpw0H5E46lvUHTVAwlHa2l5R0fNvRN9VqCoZWCicIgiAIgiAIgpWbR2z0RDNrcPfEtpwiETSNC9MOEFOAN4G7Vqg0QRAEQRAEQRAEQdAEWqRgkNRH0umtJUxJ2IdKGtGC++c14r6dpEklv+fyfsxsVCXrBUmDJB3UwPXnGgh7u0ZEvgboZGZbmtmZ5bbTlHS5pKnp/4S0HkTmNlbSkNz5wDRFpcVIOlXS11ojrCAIgiAIgiAIgmDVo6VrMPQBTgeurcazfAEGpS0XK2JmYyhsn9jqpK0wB7UwmEHAEHzbynzYu0jqZGaVlgdoLt8C1jWzxZLG4hYP77ZBPEWY2XVtHUcQBEEQBEEQBEGw8tLSKRKXAZukEfrLJZ0nabyklyRdBPWj6K9IuhZ4ARgm6VVJN0qaIukWSfunBSFflzQ03XeCpGvS8ShJV0t6RtK0tGAhknpKelTSC5ImSzqsWsElfT/d86Kky9K1QZKeTfLfKWmtdH2spF9Kel7Sa5KGSeoCXAwcndJ/tKQLJY2U9BDwh5T2J5N8L0javUrZOqb8zPLyW+n6GKAH8Jyko3Hlxi0p/u7NDHNvSY9L+mtK22WSjk1pnSxpk+TvQknnlgn7FEkTJE2oIebmB0EQBEEQBEEQrI601IJhBLCtmQ2SNBw4EhgKCBgjaS/gbWAL4EQzO13SQHxHhqOAU4DxwDHAnsChwI/w7SBL6Z/8bIlbNowGFgGHm9knktYBnpU0ptz0ggxJn09x7GJmCyT1TU5/AM4ws8clXQxcAJyd3DqZ2dA0JeICM9tf0vnkFoGUdCEwGNjTzBZKWgM4wMwWSdoM+DOuFGiMk4GPzWxnSV3xrTEfMrNDJc0zs0EpvtOAc81sQjoHVzgsTOF0AeoqhZncdgC2AmYB04AbU1rPAs7I5UGDmNlIYCT4Io9VpC8IgiAIgiAIgiBYxWjNbSqHp1+2XWJPYDNcwfCWmT2b8/tmmqKApKnAo2ZmkiYDA8uEf1eaWvGypH7pmoBLkiKjDhgA9APeb0TW/YGbzGwBgJnNkm/f2MfMHk9+bgZuz93zt/Q/sYKMAGPMLOvgdwaukTQI35Jz80bkyhgObJ9ZagC98bx8s4p7j80pHAYC9zQS5hJgvJm9l+55A8gUD5OBfaqUOQiCIAiCIAiCIFiNaU0Fg4BLzez6ooveyZ1f4jdvR1+XO6+rIFP+nmzPwGOBdYHBZlYjaTrQrUpZmzrSnsVfW0FGKE7rOcAHuIVAB9ziohqEW1I82EQZmxympL1p3vMIgiAIgiAIgiAIgnpaugbDXGDNdPwgcJKkngCSBkhar4XhN0ZvYEZSLuwDbFTlfQ/hsq4BIKmvmX0MzJY0LPk5Hni8XACJfPrLyfdesrw4HuhYpXwPAqdJ6pzk21xSj2bE35wwgyAIgiAIgiAIgqDJtGh02sxmpsUZpwD3A7cC49JaAPOA4/AR/7biFuBuSROAScCr1dxkZg+kaQsTJC3Bd4H4EfB14LqkeJgGnNhIUI8BIyRNAi5twP1a4A5JRyW/pZYc5bgRn4bxgjwzP6ThdSlGJXkXAru1Upjtgk79+xWdL333vfrj2v+8U3U4S9/JbbChEn1aXfGrqU6Fz8Fqy7+2tngFLGTZoUQ3VVfdZ9Vasq6QNLcRnTYu6CHrPviwyK1u4cLCSeWlXIrIvzsAtrQtNpAJgiAIgiAIgvaNGlkPMQiaRC/1tV20X4vD6TRgg6LzvIJBHYs72xU7c1LuuHUUDE3peLYazVQwBMsSCoYgCIIgCIIgaD6P2OiJZtbg5gUVp0hI6iPp9LYQStKhkka04P55rSlPmTgGpV0j2ir8Z6rwMz3tkFF6fe/8tpcNbSNZ7t5myDlE0tUtDScIgiAIgiAIgiBYdWlsikQf4HTc1L9Rkum90poDFTGzMfh2k22GpO2AP5ZcXmxmu1QZxCB8W8n7Ggi7k5k1eZiyVKY0vaIpMmXsjU9DaVRJ0VLSrhQT2jqeIAiCIAiCIAiCYOWlsUUeLwM2kTRJ0uWSzpM0XtJLki4C3yVC0iuSrgVeAIZJelXSjZKmSLpF0v5prYbXJQ1N950g6Zp0PErS1ZKekTQt20pRUk9Jj0p6QdJkSYdVmzBJ38fXhBDwgJkNAk4ALMl/p6S1kt+xkn4p6XlJr0kaJqkLcDFwdEr/0clKYKSkh4A/pLQ/meR7IW9R0IA8e0t6DPgh0CXJs2n6303StZKmSrpH0n257SQBzsjlwZZpZ45TgXOSbMNK42sg/uNS+iZJul5Sx3R9Xkr7REmPSBqa8mOapENzst9TOYYgCIIgCIIgCIJgdaYxBcMI4I3UCX4Y2AwYio/sD5a0V/K3BfAHM9sReAvYFLgK2B7YEjgG2BM4F19MsSH6Jz+H4IoN8G0dDzeznYB9gF8nK4mKSPo8voDhLma2A/Cr5PQH4Admtj0wGbggd1snMxsKnA1cYGZLgPOB28xskJndlvwNBg4zs2OAGcABSb6jgcamEQwFfmxmW5dc/xK+AON2wDdYdsHGj1Ic/wOca2bTgeuAK5NsTyZ/mcJhUrKM2CDlx1ZJvj3Ss6zFt/gE6AGMNbPB+K4UPwcOAA7HFSyNIukUSRMkTahh1VkMMAiCIAiCIAiCIKiepuwiMTz9/pnOe+IKh7eBt8zs2ZzfN81sMoCkqcCjZmaSJuMd6Ya4K02teFlStoWAgEuSIqMOGAD0A95vRNb9gZvMbAGAmc2S1BvoY2bZ1pM3A7fn7vlb+p9YQUaAMWaWrQTXGbhGviNFLbB5I3I9b2ZvNnB9T+D2lP73k6VDnrxsX6oQ/pVmdkV2Iml6OtwPV4yMT/qZ7rhyBGAJ8EA6noxP16hp5FkVYWYjgZHgizxWc08QBEEQBEEQBEGwatEUBYOAS83s+qKLbq5fuv1ifhi7LndeVyHO/D2ZlcKxwLrA4NTpnQ50q1LWpnZ0s/hrK8gIxWk9B/gA2AG3BlnUSBzltqlszCqjWtnKIeBmM/thA241VthKpP5ZmVmdpBZtYxoEQRAEQRAEQRCsPjQ2RWIusGY6fhA4SVJPAEkDJK3XlsIBvYEZSbmwD7BRYzckHsJlXQNAUl8z+xiYnVuv4Hjg8XIBJPLpLyffe8ny4HigYwW/lXgKOEJSh2S9sXcV9zQmW55HgSOz5yWpr6Rq8zIIgiAIgiAIgiAIGqXiCLWZzUyLM04B7scXTRyXzOznAcfho+ptxS3A3ZImAJOAV6u5ycweSNMWJkhagu8C8SPg68B1SfEwDTixkaAeA0ak9QwubcD9WuAOSUclv+UsFBrjDnwawxTgNeA54ONG7rkbGJ0Wvjyjkkcze1nST4CHJHUAaoBv4+tltCpzmf3RIzb6LWAd4KN0OX9cet6w238r+K2r4FYajjUaf+G8ppmyNt1v091qm+B3+cnaWuEsX1mntUEcNW0ka/sIJ2QNWVsrnJA1ZG2tcELW9h1OyBqytlY4IWv7lbX8YLWZxa8d/ICe6X9t4A1g/RUtUwvTM6Gh46a4tVY4yyOO1VHW1THNK5Osq2OaQ9b2HU7IGrKujmlemWRdHdMcsrbvcELW9i1ruV8ngvbCPZL6AF2An5lZYwtZBkEQBEEQBEEQBEG7YaVWMEjaDvhjyeXFZrbLipAHmi+Tme3dZkIFQRAEQRAEQRAEQRuzUisYzLfCHLSi5cjTHmVaQYwsc9wUt9YKZ3nE0VrhrEyytlY4IWv7DidkDVlbK5yQNWRtrXBC1vYdTsgasrZWOCFr+5a1QZTmUgRBEARBEARBEARBEDSbxrapDIIgCIIgCIIgCIIgaJRQMARBEARBEARBEARB0GJCwRAEQRAEQRAEQRAEQYsJBUMQBEEQBEEQBEEQBC0mFAxBEARBEARBEARBELSY/wfmHVk9yDWy+QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cS8trJO1Sfv7uUqqgtsQL36zCnm9mE4GJkk4uNTY+I18DIGvwTcGf+V1c+zoHb7ztB+Tn3u2IF3jQSWMrjRC8hldETUmeiyX9GK+A8vwJfweGNxzqyndaeJ5gfoT2Zkl/zmSl1Bg1s3/lKr4ZKc6sEnwHr1RXxTsdq2ZutRoiJbevAv9HxcT7LGA88FLy/j6eD58GHi59I3uY2UElBRBJxrepozFRJX2qVmyd5I8ukRoF/ZLc8+uIX1VkrdpB6Y3OWoo/bwHUyPzQv5vZs5LuoYFVsstllqR7KTbAC//550z3fo0OyjBJ91NpmIF3zN/GO77r5UabZgE/Sc/dgpcpWdnbUKei9E52A+7By5SZKd4XcQX2O/n3Wmq4j8Y7yfsCewPPdGAanC/jz6JifdGEfzOZ7POp1Cn/jY++PZFkm4orEOcAt+Hf+zckfcNK66KUvqePAzfmnLPOz6tJ5lXw+mWvDpLoclxxeTGQWYw1pDwrN7jxBvsnqVjQvUHFou7lXCctb67c0TdTtdPeSdlXVlq/nTv/CPBP3KJnWAePs22uvFme4vupxYp4WZ2NpLZb3Ghh0+zsv6bCsIPnGJKrx0+WtEE94XST/Dv4RcmtfTAHaKpV/nVixbhBurY+nlcfxdtEEztQ+NyD5+/taKAsyOW5TJ58mX45nvcvxq0Q8mX8cZIWUFGWPV3qtLcBf0wyja4Rf608uZMkS2Esx8JTsk7H09TwdnO1+mivUt30TqlMrcX7+Hf7eWDHnFJ0c+BbVL7nzwI74wMMk0rxb2VmP5H0VnJvyX/rpfzZyPS9t/EyUfjgWV7hcDOu9G3B2zCfwts7KwPH4UrqPwCbygdrDC+Djyv5nYB/+yvhdVH2bVWr4zps03f1W+8OnbQ7HpK0Znq+wWb2cLrnaw0O2D2J55G/55ULKe4P4u31Fczse7hix/ABjg4JBUPQG2QjKAPxBkmB9HGCZ86xkj6TjsfhH8gH8ZGYWrxsZn+VdCRekF9NpcN4Al6ZXJx+43NxbgdMpFLI9scLp7uBK/ACIlMEjMYbqP9Ovy/nCtkdS5XJLsANSfZ7yw2jFHcWzv4pvg/iDZt56fjOTp653CnbUNK+yel2vFF3dEqD96lzTr8aM917P3Xaj09y5+O4JsWvdO19KiMWXwU+hHf+hyb5/4an28wUznvAL0oN2W3yja10TzUWpAZOplCYmcKaiSuO8h3jbajklw1xy5l68l1rev5tgTmSsrR9D7gz1yg5OcnQbv4tKVsEZz6ez1egUgl+xipTRfLWDMfhaXkfcE4Ho5z5xt88cmsuAJ8zs39kHtM38B7e4P5auucQvFL5XKkB8at0z/FJ1uupNDbIhVlefOlVXIFGZ2lZaow24Z3LQcCwUuekMyVlPsyL5JZMzwBbd6JwuBrPF4OA2aVn2QP/Nj+S5Pp5Lo6yCX6XGhOqjFQKeAq3LsksgN5tIKgRufJtDHBdnfEfhzdIDwaeKj+XmW1Q7TlTvj8Nt6A6ELgf/37eKCnd9ge+gM/XPAofjcrSfC4wXdLn8e8fqnTeOiMn+3T82/4V/t6HAr/GO4Zn49/T8lQ6nvmG83C8w38r/k4+mWTLLJ2Ww7+xzyc/d6fn/xy+cNi3c7I/gtcjd+J5MTu+H280v4HXCx/Gy4fMaqLM+/j3Af59tc9RxkeA26dFmNkpAJKGlRuj6Z4H8A708urC9LCSUum3eHnwKN4Z2As4HV/RfWZK25lUTMp/QrFzl6dqp52FO7f5One3klKyMO3NzH6X0uPEDh7nDTz/no13rq4C/h/wn06SYQfgh3ibobygnCjWh/kR+wvlK98DNZWZW+L1+Jfxd9a/FE4h7XJl1kKdUjWgnMfz4bv4O/iCpKuplL0fk7Q2cBiwBhVrqYWm48hX7O/QihF/t+/g38IzuLWp8DbRbrk02BrPp3fg1neNMIPiN5Ln63i+PABP59uplAX/xL/tTFm2Q67TPhj4DV6OicrIbUdsVOr85893x8siw62qfo2XmUemDu9M4Arr2DQ9P2VxbYptqcsolqm1aAE+hn+zD5ee+TK8fJqJ139/xi0ZDP/Wy4MMNyb3F5Ofp3BLtTyF6Xvl/EqljAIvr/+Bl0mvU1Q4bAn8FJ8aIby8OQhYE69jtsfrmx/hytZZ+CDNp9Nx5ndbvF3xB9yqNJv68gSwa17wVMfNx8vdIZJOwfOXpfR5lOrfOvS8sqGsIMszFC+TrgH2l7Qb/o7PlTTUzH5cZxyP498EkjY2s6nQXr9+3cx+LulLye/qeF75GK6YX4hQMAS9wWvAV/DGZEfz1YZT0ZLfD3wD/2hWzXXQTijflKswBYyR9E6KawO8A5uZvZPCPBtf6G75dH3X9PsblUJ2ReCh1GiZjxdWmSJgJDDAzK5K8X8uyf5RYASwUq6Bs2upcr+Fonb6pFw4n8495zW544UUAeXOP17IZ3MBj8MbuAZ8E9+iJ+uMD8Er02yEPB9muWNTMN0ry1BiTmr8/DfeKcyPwu+AV8TfxyuWR6iMWAzFLUWykfGDU1pOS7+Mo83smFxD9vuS3qfS2KrFAXKz/pkpvj9SMXkumx/+Ae8wnQ58qpN8l5/Gs7qk0XiFt4qZnZo6U8Nzso7EK5uTqZh/N+ON5MxKYVe8UloVrxDvSBpo8I7G6zm37yc5vynpXbyTnymu2hdyxBu85yT3x/CKZgPcrHNSCvddXAN9bOrcX4SPFt1ErgEh6dokywR8NDfreLdbJyTKI1/fxfMAeJ7Md0oL5rN4QzNrjL4D/MPMHkodgqakyPk2Xsnn30dV83zc7LwJb+ifZ2avlhQO+RGuwSneeXiH467cs5yMKyovstICRqmCnUPFBL9uSh2/A8xsr3T9ZHyrp8wC6Bt1hvMa3nB6ltRQlI+EvZLcy2aw+Q7af+GNyJ8Bu6g0taB0fr1y64LgjePtgYeAR8zsohT+CSpOi7gez1N/TnKC59sszf+Gl9U/x7/J7fD8/gNJ95EzFcfLkQ5HiXKyboN3/GcDe5rZjyXtiY/M/RnYzsxOTveciCvBP4SbbG+Af3N/xuuB43OyfSNrpEkamBpcG+Hf6XV4+ffDTHa847Uf3imbiiv0DsPLolF4efZ5PF82k6wmJD1CUTFwFf6dfB5v0P8X/p0ekdIvmxYxL9cp3QOvdz+PlzHHAK9bMjVXRaEvfB5zloYLmaOrOM3rM3ij/NQU93QzOyv5+6aZrZy+1/OSjPOpmMCXO3d5dkhp9n0gGxkF7xSXO7f9ch22XfA8dWBK07uodBjzioI7SsrDzc3sK5IG4vlwePr9BVfolJ8//z4eSe9hNvCHkvvI9K6y+jAbsV8PL3cnkzpPJXmOwBVjq+EdhINKlol74h3R9fF2R57xeB49FyjPKS8o5yXtTPWBhB2Aw/H3MDKVvV9L6Z3vFG5C7ek4+TLs6pKi+J9UlAp/Tv73TWkzh0qneS6u3J2NlwlVUWk6CZ63/ppk/RxetwFgZmclBcijuDVTS04JdSWe/6bjdbNRUXpuhX+Ht6Xz8/G6tSMLoG3xTnDW+c+fN5lZa4pvHrBO9p5TWTQROEduibZLklOkKSuqTFl8lUpbSng9lpWpq6pilTA1/efTp1+uHfo43mnNFAr5eu77eF77pZnNSP4zi4VJkv6Kt91/mdJp9STz+OIb4kjgaDwPDE3uWbvrGxQVqOfh5fe96ZnG4wrzHYEXUjjX4GXz1em5JuLtx0yBsKWZ7Snpl3j5/T1ceZD5fRn4PRVlxGa4ovZKvL+Q5zoq7cdf4GXYRXjeWJFi2zdvnTMnpclkurG+SAdlz3y8DD0Q2FfSLIoWFO/jbebX8e/wklTGDagRR7l/MT9Xpn8bz0NI+l76P55KGTQ0uW9bNXzr09NKgiWRlDEN/5hXwQuwfMfiLktzsZJf8AbBODM7Ll3fwczuK4V7EhVzq/HA/eYL2W0M7FWqlM/BC6X7gDPM7BOSvoB/FP1zja0TcQ3n0PS/j5ntl9xOwDtqG6dgd8W1tefgGuPVcfPzMXjFew0VrXnWIc4aZ5Nz4ayIa2fBK9XMhP01M8sq3ky21sxUKTXcn8VHA/vhheonUlzfxrWrb+KNnL3xCn0KrkB5LRfu9Wa2by5N+6dnN3yULpsHvRpeuF+Xc3s5Xb8XL0w/iI+AP4RXGFma74I3ENbHtZtb4o3CTEnwAbxyOiGlW2uK4wspjjNJo6NmtnNO9tPJdZ7S5cyscA8zOyjnd6+UvtvhDek7qBTITXjFtADYwMy+m+7ZkZwyIcUxK9dQ+wueJ36SnvGDeEV/H7Bv0vAen94P6fxwvGIYkGtQfL2UX/fCGwkfB/5VchuMa+/vSWnyR4prj3wHHx09CVfUbQE8l/7/BOxmZj/LaaC/YGaXJbmWw/PN5fiWQ0PwhvxdmTxmdkyK5/j0/mZQ+Z43wb+JtdJ7eRcfxb5a0rfwhkJeKbFqeo59cMVWVpldCdxhZv8r6RO4Eu02vDJ9C2/MtM+FTLJMw7+BR3LH91JpDI9IMq1F+g7wTtKjyX1f/BsB72DciOeVtXBF5s3pfyM8j2bPvGt6hrYU5lbUObUgVfgX4/t2X4U3bJTe3ReolAX/TUUZ9zTegMmHnSka70iynYeXs8IXsTo3pdGO+PeZ3decnv9tfBToVnykcRo+GrVq7rnWLp3vjTf67sS/3ayhth3+nknp8wH8e34bb5zthuepoRSnKg1P+XJLvIPzGp4fhyZZp1tx+ty1uWe+ML2v7Lmm5mT9MK4gbrdYkfSpJMOHzez4dG0zM3siHe+Bl6ttyd+QpJzIyzY8yT0Sb5Rdjue599JzHkbl+3kuvYvl8Tx3Ubp/23QMXnY/gOfZfEPssymNT8DfsyW/f0nHG+GNzdXx7/EsvP77Y3qWg/EyrDXFfQBe52XP+t94PWL43OSsztk9X34mv6tRUbo9hHcu70zhvokrc9qAXVIHbgu8DLs/pefqeH78Ua6c+jFep+S/5y+ltPuEme2S4j4Rr0NIsu6Ol72z8W9uCzw/3puufzVXr/8RV45+1MwOljScyvSN2/BBgmfx8ur1JDPAX0t15UB81PBbeGfhGjwvZHXPa1S+50vxOm4KruzcF/9u/453qu6nYiJ+VU6ej+Gd4rbUKTg0XRe4tUPKv+1pWXpHWR7dKL2n7Js4DG+rGP6dzsy3Jczsl7kw8nXMt3BrzuVT+vwl84Yrcv6LSr77GV7GZnH+L5U1Fd7FlVz3pPT9f/i3sx9ufj8Zzwfz8DLkbLzM2wr/frPnzyz/MllrKWmHpvdh+GDF6aVn3B23SrsML+ezNtnGZtahpWeKbxqVeuUsvH3yNm7tl6+rj86UreXz9C3tk8L5K7CFmf01ue2D10Vfw7+pQXg58jIVZVrGSnid2x9vX2ej6uB57jNWmZKAFS1AbzGzKen8jBQ+LNx2+jT+fW+IK0YukK/xtSdejv0a/4Y2xMv3Cfhg1R0ULXeHpCD/k2QdgbcRFyT3B3Oyb0l672Z2j6Q78bx1L16PvYF/S+vinfh30n0b4OXti3idcDqe51ZPfjbL+d0Bb6utmeLL3vnngN9Z0eJzz+RvuyTvP83sZ8nt93id8ySVbz1r6+6V5LsPOMzM9syF+TW8HnsL330hG4zJ3LN2vOHf0Tp42fJ1M/tmrgw9DK8rTwFOxC0PXs4FNd/MJiYl+CEpXReyBpV0fKl/0ZLLr/+Hv6t38Xf3YPqfa2a/k69L9CHgI2b2JzogFAxBj5M0qHOpjJLcTaVh+HW8gzQOr2A2BP6Vbt0KbzxMxj/e7SiOTp6DVw5n4KP3t6Xwh+If3bvgFZJ8lHl4urYvXuDMNZ+rvGmukN3MzJ7IaTwvxRvg4A3VuSmMnfDOWhPeELhc0vl4hXgO3mi8Kd23OjDJilM41qay4OD2eKNucHrGbGrESlQUD4Y3Xq/EKxvDG5X9Ujo9kmS6O6XBTek5X8E1tQekdL3HzFpUnJZyPF4JT8ILsNF4o8/wEeizcY32GLwS2Svnlq/s98dHIDMFzZ/xTsPjaWThA3jFknWgbkhh9scbth/B3/V26dmnAo+a2b9zBenPcKXPzPS+sk5y1tkVPso8M1Wg15nZP5PfE/A89z4+CtuMdwqOxRt7H0ry7ZPSYkxK39+a2b2Svo5XkO/jlW32Tr6GVxCHJBmyPEoq1Eel+3YrvYMd8fySmRJOwUeZHkiNmP54ZfkHvEKbmp7rFLwDMx1v2M/E8/Y30jvon9JyHl6xrYl/R7/EG51Pm9n7krbDK/7HzOyB3HtE0h+SPFPxb+WgFO5WuCkxVL4n8Ar5dryh8bN0bVW8ohtgZndI2pSKKWE2YvsbvJHXijdcMxPYkXiDUbhVwr9SmmffxHC8svsKntcXJLdT0/vIzM1/gHfGWvFvYSsq39RQvFFxZpLncryhCd44ODQ9qwE/MLORKW0OwL+37Jm/njotAr5vZmfKrSR+g5uO70ul03FFeu4sv6yajo9Oz/E+lc7b2viI+n74qNW4FP/JeIck63ieiDfkM0XjiJQ2q6cOylfwBvwT6XnfwzsNv8QbQP/By5Kj8BEew5UmmUK3GS+jXk9pOAdvWL6O54mJVJRwW+Hl02NUyoFr03tcCS87JuAj+k1UTHjXwzt4C6goHVdOaWP497krlYbhaLzB/bf0zFn63YkrV3+e7svKx1nAi2Z2Q3pP2yQZdsCVZ7ul/2fSs1hK+/np+Jn0PjLZ5lEZCcry6HTgf5L/S6iMnGZlWVa+3J6eJXs/m+Llw2y88fsm/h1fl7v/R3g5PJBKGfZfVDo5b5SU0ePTe8kazuenNJuflPBHJa+7pXgnpPND8PcF/n4+guf9/yRF37NUlG6r4vlwC+A5M7s2PYvS82yFdwLArRF/mWQ7Ec8f/akoP0fhefZ4M9sj9xxX4J2ENrxMmI83uO9KMqxNRcG9YfKX8TyehwfieeINYH0zO14+v/5UvFxoSv7vTv8/wDv5WR3XQqWRn5lb341bwfyUyjcKXk69hTf2P4O3H8DL+FbcvBq8k/iBlFa3p/BPxfPQOmb2zfT8K+Blb8ZKKfxMofEZXLGXkbWx1sTT/2UqnY7XS53fL1BpS2yD55fs/hl45/NtSZvg5eFIvEy9Pf/MZnZkrrM5Gv+Gs3LpcfxdQWWkOivvM0vK6/B3+DieF99PMmTtvV3wtkR7fLnnRdJIXBn+O2C8me2qyg46P0/xNeEd9h2oWIp8EVfcGr544F1UppDMz8kNXm5lbcsd8bo2a3edgNf386gslvw6XvZsgrdphOeBnamMEH8myWb4yPjUlMaG59U38W9jzxTu83ien2dmZ6ZnXwevB15LafUG3t68Dy9r7sPLzG/gdetzVNYgWRn/Xt6hNKUmKbD2oNIO+gCV9V6exuuP+0kWDZI+iZcHhu+gc1xqbx2L16mZ5e6X8fqDlCYDc2mcz9s74G3RI/B69uacv0yxlbUlwdtJ/fG6ajCVegIqeZr0LJNzfp/Ey41MGbEjlTJ1NP49Z8qR66goP4S3hbPjzc3s67n2fTagmsU9En8vw7N3l9J5IK4UHQocY2aFKVlpAO0HKZxb8LJ5Zbz+uJpUr+Hv6uUk58CUXjtQaftlg4Qb49/6SVTa3jNyz7wd3vYAz8/Lp/uHJD+yyrSIqeTaj5IuS2FdY1WmXzV1dDEIuslleGPwKrxg+C5eWK4H3G1m1+IF+jy80XcVrhmblrv+saRJE5U5n3NxxcKruDn1JamAXBdvLG+FdzZIcX0FL4g+gSs09k9uK0j6aOoEH500uX/GG1uv44XMHHNT2nfM7Fy8c7xi+s8anX/AtfKb41YZl5jPo38bX2xmOypWGy3JbUjueBA+gvUqXkg8jBfGc9KzboZ3UEakNF0t+R9GpdK6A1e0HJPSazO8IFwbL1j2k3R28rtbknuVlK4r4wXVXOArZtaW/H0Lr0gvSXLl3SbgHdAJeIHzYbxRNQyv0MbIp5J8BW9cvZbiGIkXZMILv4/hHczD0zvKRmu/me5/Iz3Hl3Czwc8krfocSVfhBeF2pLUJ5Ga//8bXyfh00sZeiVd42+ONWVExaVvezC7EK9L3cQUWSbZtUiPrVbxhNDel1YeSn7PxPHtrLo+ujE+R+HVKh3WS29a5d7A93hj7VnruL1KZp3oLXuHNwRs3bXgjGbyRsTLe6Pg33ih6Ksn+sXTtL3jjYAW8QXA1PkWhLcVHSq8m0rxrSUdJ+nLqfDTn3sGZOXmGpHC2Ag43s5fS+8pMdgcD08zNLrPO3YaSvp+UeDfhHaPXU5pMNLMfpLiOTOnbSmWqQjPeSLwwPUN//LvLTO5/hFf8m6U0+L6ZXWRupvpvKovtDcK/l1m573IB3uh8P/3ey7n9G59ScXE6vy+XNp+maAK5SWo4fQn/zj9HboHZ9A4fwDucTRTzy1kp/PH4t/023uA5GC+jPprcVs3Fnymenk/3ZJ2Q5dJzHoGXt1cAmNn/4OXCzJR2D+AN32Eks+D0vuak9/FgSrPX8e9195T2q6R03ptKg3oGPhKapdsUM/tskiMrBzbA8+4qeDmwK75X+QX59E7/y6c0vxhXtl2UjvfG64a18JGjH+DKjj+m53wfV7L8vxT37lSmSCxI33bWebg5kwFX9ozHy6LxuNVcFufuueMtSrItl95hpuQ9D8+3b6Tfw0m+rBzPly9X5N8PrtBsxfNVZn3zFpXpYo/iZdddFMuw7XPyfaD0/a6FK0Am4mXCqPRO10iN1stzz308rkBZBW+wXpHcLsPrvj0kHYHXoffgefhIXGk9Hy/LN0r5fgu84fo1kvl1yls75GTbDi9LX8LL7SOpLGQ7vfQcK+IKkmnAqFSGjsTL0S/hZe0b+HeYjaauZmaX443pY/HFj09LZUJmMXOKVeYhz8K/0+VS3i/Xf/3xAYOLcIXc/JRuUyl+oz/FLRr649/xdcCVWV7GrSey/POz9K4fwsvTE8zsx+kb2BDIpq58Fs+n+TbBVfi31w+f7nIZ3kEZgXcgs/e+G5XBhbeBt1M756PyaX5X4B2Qy/D8djdej08ARiflwp7JfVx6B2eVn1luHv8FfMBlNbxc+n9Jnndy6TqTtCVgSsMz0u8JvDwyfLQ9s9p6IMmyVik+SdpO0rFpBPgIXLF9M7CWKlNmMsuW95Icx+bSrpni+icL8PImez9fxsut9dP/3Sm930n3HYorUQ7Bv51/pHyeddYuwevo0SnMrO5/N1enzMHz8qrpnrXy3zNeZmbp+kB6x68CrZLOTM++Dp7nB+DlxB/xtvHR+KDNUbgS9jW8LF49K3NT/h+ELwQ9Hm8HD5Z0Lv7N5dtBQ3NpszWu7HkI+Hwa3Bmdk30TSQemNH4opf8Xk7/xuJIysy4r5+05eJnwq/Qcu+JtjqzdOBsv+7JBv33x9tUKeF5fkYoFqyV58t/Lz0t+/4tULlLZ/cDwNvyJuHLrN+aLyZ6Cf5snpePVUtgr49te5tv3k6i0c/Y2tyb7JwuvQ/IlfCDnZrxf0hFZHnk8yfUUbh17EV5mr4BP/8vaPS14Ps23/W5K6fdXKotTrpnSdnxKi8vx8uMxPK+14JZuWbt/N3ztieNxZcl6qU7bJoU3BX+HmRXQQoQFQ9AryM32tsc/wGYzuzLndiQ+wv+4pK/iBc98PONeaGaPJ3/H4pm+X/r/IF4AtuBa08zcfAdcU/wuRTP0zKwwMwWeaj6yul5O1C/gnc+MfUhme2Z2ZX7UF698huFKh/+TTy/I5gpmnRrDC7d3gZWtMoVjFN5BeRgvJF/HNZCn4525aXilk42ogjdCflZKy9Yk30C8oM0aRQfgFUs2Mj4KL0Qy0/AtcHPzu/DO7e/wKSb/SukxwMyeTCMTh1nFrO6nuOlY5paNWBlu9XAyFVO0zBIDXMv/grmp/B7Jzz+oaJN/RMX64yd4wX2Pmc2WNAB/55/FK/qvJbnnmluFfImKKfL/y7lviFdWD+EdxndzbtvghX2WHoclOdvwBvS9OdkOxhtL55EsDLKHkq9N8CW8YjwkpUUL3gh5mYqZ6cDklr2Ph/DG1bN4A+ZlvMHd39wENjOj+wewppn9TdKuZnaXpEOy70cLm+1dZxUzy6zCVHpPfyyF82kz+4Okz1qae5p7rvNwRVX2DvLyfJbq39Z2eMf5f1NDbye8cbQLcIOZ3ZrC2hAfsXglyfc3ctOaJF2T7svM/C29l6wB0oo3iu/AO55ZGoyiYuL9avKbZwW8QdmW5L2YygjXwCRTdu9z+Hcm/NvITMqzjk1mArmumZ2W3L6OK0MyTsA7uJmVzUfxxtE7eOMgM68emWS9no75lHVgsitp6yTzBlYcnRyJT4W5MpcPwBsqf0zHX6Ay7UN4RysbqfgbnpZQMcefQ2VBtr2TW3NKpw3SfYOTv9vxsnNqymtZ/hiKf9s7p+O5ued/Pf1WT2Fvj3cc2vD8cxn+robj39gNVEyMN8LL2bPw8n11PI/MxxtZH8FHWu5L6bN2kmFnPA9lI03DqUwrOwgfMWpL6fbvnGyv4mVGPo/+JSfPoVTmAwtXSJDCmoWX2dn7uZhKOfAelTyQ5Xty569SKcO2pzKlZ3bqVJOe7woq883zI5OZpdebufC/hddn9+J1xItUrMFG4Z3yVXDrlu9aZZrXUSltr0tpPyEn6wnAzVaxDMxbCX7MzG5JxxumtJ2Y3WfJ7Di5f4rKYoPz028o3im9D+9c9sOVGnfly7N070t4XhmbgpxiblX2GbxMfzkn8wAzu0bSIcATuTrus1Yxhf5zinNlvAO2fu5+4XXF4yld98OtxU6Sz1mebhVz42vN7IB0fHJ6rqxcOhjPSyvgbYkD8DJjWopjD3L5LtUXWf2+DxULrbKZe76dA55HX8Drjc3xbzZ7jm2T27pJhrvwjsNAfNQ3/8z34+XaBlTmw6+Z0uepXLp+G1cOXpMGcwbh7253vN7K1017UsmfX8XXlMji+zbF9ayOwtthj+NKsP+YWyp9kMpULfDy7kt4J3M6/q2/ncK8EV83K6tXL8yXt2mgIHuvO+B1z8v49zEJGJir897D8+Ofgf3N7BcpjENS+mXtwlb8e27C69pP5+SdjSuAsnTdJb2DrM5bBy/TmvEFzq+TtDleJrwP/MHMFkj6Ha4Iy6YkvEFlZyHhbc3MUuQw/DvMrO6uxEfxJwOHmNlluefIrCAy5udknwvcbm71dFTJ38FUpjnAwnl7Ts7v+ildPmlmX0sKlaxM3zr5uQ4ve+bm7jsEL8+eT+37clvi6Zzf/6ZShkKlDNseT/tH0nkzrvR5jkrenkGlr7IJvjDypUqLIebaOePwsviDSd7MYnlviuX75rgly164pcOlkjJleVN69jWotPUepFIWfpiKNcjs3HHW9nsCfz8fTWl3C5V+zW25PsUX8fedvZ8ZVMrJASnelVN6r4/n9eWT+/34AMTALK+UCQVDsESRGgD9AXKa7+6E9wErmSk1cO+aeAWAdbzyb7eQtLyZvV+n343xynx+Oh+QHafzcZa2rqkjrOWssrBP+3FHcVpaZbYrskuSdVIASdoeeKCav7x7+V2W3Arp0RvI5z7fbmZvp/Mej1PSh/LnPfEN9CSNyCdpBTN7p5p7B/5XxCvj++uNI923fPJX17dUJYw1zOyVOvwtj4/gvSxpOWCQmb3ZhfhGm9m0Gu7NlhYMayDMmmVdrXeXlH4ZC8xHe3scueWQss5pPv7e+n7Te5L5NKL2496Iq1E6K//qDGOEmb3bA7LUyh9111WlMLt6X/v6GUsCHdWpPVEuleJoqDxdXEha3cxereG+ULsipyAcarnt8zqJp8ttu75CVueZ2e9z16o+l3wB2nl1hj0MVwa93annRUBSiBmuMJpTcuu0XdFZnVlH/F0qizoJc0fcwuFj+Hozl+bctsYVLi/jgzTPlO7tkbZeT4TTSL4q3BcKhiAIgiAIgiAIgiDoGSStab4u3Egze72Kn+3N7P6O3JZkQsEQBEEQBEEQBEEQBEG3iUUegyAIgiAIgiAIgiDoNqFgCHqF/GIv5YVf6nXrqXAWRRzLoqw9FU7I2rfDCVlD1p4KJ2QNWXsqnJC1b4cTsoasPRVOyNq3Za2KmcUvfj3+w3eJWOi4EbeeCmdRxLEsyrosPvOSJOuy+Mwha98OJ2QNWZfFZ16SZF0Wnzlk7dvhhKx9W9Zqv7BgCIIgCIIgCIIgCIKg28Qij0GPMkADbRBDaWEe/dPWrPnj/Ln69WN+2xwGNA3GFiwouLWsPQiA1vdn07z8EPo/N7dqOLXi6MytcC7RYnPpr2zbYmixefTXQLAGwqnXTRTjIBd/+i4zvzZ8iJ/Pn0X/AUNRSxstC2bTv98Q5g9v9rSaPYvmIUMZ+Poc5ttcBmgQ1tbWI7Kqn8cxv20uA5oGYQtae+wdVM0fTa7/bH+WIQOZ3zKLAf2Hwsw5xXuHDGpPj7YBft+CebPoN3AoTe8lv1naDhvM/PmzGDBgaPumxC0ts+jffyhNs+cx3+YwQINpGzqwmObvz67EN9S3gG5ZMIv+/YZiTSqEM3+4aJ01i+ahQxn48uxC/PNGDWl3G/TKnOIzdvC+8vmz8BxmDeTtYl4uv9uq77XGfT31nnsynHrjkMR85jGAgVjpW5ea2t9HRvv7yb7LDt6B0vtpD7fW+8mnK9QsX9TUVF/+6MV07ZFwVL1863OydvIcUP932KX6Z3C6N5VpzJlb/fttbm4vswCstbVr5W3+m+gD7yffPsgeOiunaLPCN2ptbb1fZgwYwPy22QxoGoLNn190659ry7Qs6HIc5fcBdZYni+P9DOjP/NY5DGgejM1vqfHuUp6s8e1XbRPVanc14ncRpMeiCmdJknVZfOalQtZadVzJbYa9Pd3MVqED+nV0MQi6yiCGsrV2q1xoaq4ctxW3bW9esZInW98sbhX/6s83LJyv/omnuy9c+jDaKSnXNLDyodFalDVTgPQk6lf6/JoraWXzilvOzt1pXOF88Muz2o9f2md4wW3Nn09uP26bPbubUibRVlipcN46/a0eCbcWTUOGFs4XbLFexe3+4hbo2riSX2aPGlJwG3LL48Vwttq4/bituZgnBk36V/vxnG3WK7gNuOWR9mMbs3nBrXVQ8V2+uE//9uN1vvP3gts/v/GR9uP1vj+l4NY2axbVUP8BhXNrmV/V70L35vJaOS/n323rW8UtsZXPk73wDSwumgZVlAfl59LgwZWTcjlQPs99p/kwAdry33C5rCl9+7XStmlwJT/31Pe8OMjn30bybl+jUE9QKqs7qWPqDneDdYqOU/5RCbL8/S5fLP9b332v7jjz5PNv29y5XQqjJ8m3D7C2gpvNnlM475XvIt92Afp94APtxwumFbeS77fyqhW31zrcia4+cvmnqZTPar6TWvmuG3myFv3W+GD78YJ/v1Jwa15xxcJ52/vvV6KfX/r2c/I0Ui4u5Lct91yltmawBNNT+beXvoPFTo0+Vlep1dYs13+3z73ixaqi9Yg0SyCSZvZCmGMk7Z07P13SST0pk6TjJT0t6QpJn5C0Uc5tvKQDGw2zTtn2lXRKT4QVBEEQBEEQBEEQLH0sswqGXmIMsHdnnrrJMcDeZnYo8Algo9reewYzu97MfrIo4gqCIAiCIAiCIAiWPELBAEg6WdIjkp6QdEa6NjpZClwqaaqk2ySf5Chpq+R3oqSzJT0paQBwJnCwpMmSDk7BbyRpgqTnJR3fTZkuAj4MXC/pu8C+wNkpvrW78ZzPSPpNeo4rJO0u6X5J/5Q0Lvk7XNKFVcI9StIkSZNamNeRlyAIgiAIgiAIgmApZ5lXMEjaE1gXGIdbIIyVtFNyXhf4lZltDLwLHJCuXwYcbWbbAq0AZjYfOBW4yszGmNlVye8GwF4p/NMkVSZnNyiTmR0NvALsYmY/Aq4HTk7xPZduzxQOkyVNrvM51wHOBzZL8h4C7ACcBHynM3nN7BIz29LMtswvGBIEQRAEQRAEQRAsO8Qij7Bn+mUrsQ3DO+IvAS+Y2eR0/VFgtKQRwHJm9kC6fiXw8Rrh/9XM5gHzJL0BjAT+00WZ/lbH85xsZtdkJ7k1GDp7zinJ/1TgTjMzSVOA0XXEGQRBEARBEARBECzjhILBN7s5y8wuLlyURkPB3r8VGEz75jh1Uw6jnjTvUKZuUu9ztuXO24g8EgRBEARBEARBENRBdB7hVuAHkq4ws5mSRgEt1Tyb2TuSZkjaxsweBD6dc54BLNdbMpnZGyV/jcTX0HP2GDW2TSlvTZln5n+W73lZOtmWprw1ZG+jAQNKFyq6q7Isg94obsvVNK2yLdTo/3m14NY6p+i3J2h7b0aPh9kZ+S0SAQZMq+SXBaVty/RkZXvJlvXGFNzK22I9d1hlZtia15b0hbkteAY/8I+CUz4na0Ex/pmjiu/yw9dU3zbtR/tc1X48/pudLp1SiXNAcXZVI1v91druy2ZWtsZsXrW4nXHr6+UiZ+mg1pZvNqNreb2Rrf0a2fIzvwVfI9u49TWW5K0p89SsJ7qx9Vk+3H7vFPPgglrfb0sP5YGmRTBjtrT1Y632gYZVtmdtWW1Ewa357/9gUfP8zyptktGHF7dCfuWASjm+6q9K21Q2sD2e+lXKeK35gaLjs89Vjkth1NrSsuZ2l43IVtq67qWDKttUrnFu0ShXyxW3mG4eWtn6d8GL/64aR2GryU5oGl5qI+bubX3nnbrDKbO4t9OtuaX08sVnbs1t/7nU0lPbSfbhbSmbVym1u6ZPL3qoIXvzsMq3tlB+yH/fjWyZXKOtudA2szVY5hUMZnabpA2BifKXMRM4jGJ/osyRwKWSZgETgGzj6buBU9LaB2f1gkzl1v4fkhzHAwdSgy4+ZxAEQRAEQRAEQRDUxTKrYDCzYbnj8/FFDstskvNzTu76VDPbDEDSKcCk5OdtYKsacW5Sza1emcxsdO74forbVB7elTApPufhueNpmZuZjQfG15I/CIIgCIIgCIIgWHZZqneRyC1w2JNhjgG+m3ZpeBI4Ani7G+HdlBaOrOVngqQtO5JF0t6584W2kqx2bxfkXEPSNZ37DIIgCIIgCIIgCJZFlmoFQy8xBt9FYkyySLgSmFX7lgqSViptI7kGMEHSSl2UZe/OPPUEZvaKmdWchhEEQRAEQRAEQRAsuywzCgZJJ0t6RNITks5I10ZLelrSpZKmSrpN0uDktlXyO1HS2ZKelDQAOBM4OCkIDk7Bb5QsBZ5P6yFUk2E0cB/wAGDAfsAIYHcze0vS9yU9I+l2Sb+XdFLu9oMkPSzpWUk71pClVhrsmZ7nMUlXSxqWrk+T9OPkNknSFpJulfScpKNzafVkI2keBEEQBEEQBEEQLDssEwoGSXsC6wLj8FH/sZJ2Ss7rAr8ys42Bd4ED0vXLgKPNbFvSQohmNh84FbgqWTBky8FvAOyVwj9NUnEJziLrA781s4+Y2Ys5GbdMcX8E+CRQntbQz8zGAScAp9WQJVM4ZBYSW6bwVwa+hysztsDXjfh6Lvx/p2e9F19r4UBgG1yJURNJRyXFxKQWFu1ODEEQBEEQBEEQBEHfYFlZ5HHP9Pt7Oh+GKxZeAl4ws8np+qPA6LQmwnJm9kC6fiXw8Rrh/9XM5gHzJL0BjAT+U8Xvi2l7yzI7AH8xszkAkm4ouf8pL2MNWa4ys2OzE0kT0uE2+IKQ96ddJAYAE3P3XZ/+pwDDzGwGMEPS3M7WiDCzS4BLAJbXin13L5ggCIIgCIIgCIKg11hWFAwCzjKziwsXfcpCfsi9FRic/DdCOYxa6VptvYbO4szi6Cz8agi43cw+00n4bRSfp62L8QVBEARBEARBEATLEMvEFAngVuCI3JoDoyStWs2zmb2Dj95vky59Ouc8A1iuF2S8D/hvSYOSnPvUcU8jsjwIbC9pHQBJQySt1zVRgyAIgiAIgiAIgqDIMjEybWa3SdoQmJimB8wEDiOtrVCFI4FLJc0CJgDvpet3A6ek9Q3O6kEZH5F0PfA48CK+RsJ7te+qXxYze1PS4cDvJQ1Ml78HPNsduTulqbly3FZM7uZVVmk/bn3zzYLbsA+83/OyqGQkYsXZHBo4sHLSWpTVFizocXFs/vzihebmjj0Cc1cdXDgf3LJG+/FL+wwvuK3588ntx22zZ3ddwBxNw4t6rNbpb/VIuLWw0jtoGV3JL00vv1r0u8k67cf9Z7UV3DRgQOF87f+ruLc1l/LEvIrxzpzt1i84DbjlkUp8/Yq62WEvF9/l8wdW3tc6jxWj+O5fK+uxrjdwSsGtVj6z+S1V3Toln/dL+d5y522vv1G8r8b3u0STey41FfNAOb/kKX+z+fel/sX7rKX0fRciqV0WFbz2q1TTvVEOLSqWmufI1xOA5cqMRt7rQuHm8k/b8KFFtxppp/491Ixra+vcT7fjqL8MsRmVXcb7zSjuOG5Ni35s7MMnvdt+vKBUr65+5dPtxws9YQN5wBZUynh7sTTLtkY4bXPnVnebV2NdrEZkK5VnH7ri+fbjBSq+j7Y3i+2Dhdo6PUDbe8U2Yrm90FVqltuLgFplY+v7vdAu7ut0o0ztlXB6gXL/p6F7Z9bYxLCLz1irrblQ+6h60bN0KxjMbFju+Hzg/A68bZLzc07u+lQz2wxA0il4hx8zexvYqkacm9Rwm5aPL10bnTs9x8xOlzQE+BtwbvKzc87/dNIaDFVkGV8KP3/vXR3JnpfBzMbnw8i5TS/LHgRBEARBEARBEAQZfX6KhKSZnftqOMwxkvbOnZ9e2hISYJ+0E8OTwI7AD3P+H6ALSDpT0u41vDwn6R/AY8C1ZvZY2UPaonJqkm3b0nMcLunCkv8JaYeKbiFpDUnXdDecIAiCIAiCIAiCYOlkqbZgqMEYfPvGm6p5SNs+XlXFbbtagUtaCbizA6fdzKyWbfnTwElmNqmGn0NxS4fL0pSHms/RU5jZK/jWlUEQBEEQBEEQBEGwEH3egiGPpJMlPSLpCUlnpGujJT0t6dI0sn+bpMHJbavkd6KksyU9KWkAcCZwcLICyCZEb5RG+5+XdHwncsxM/ztL+pukP0t6StJFkpqAd4HJuAKnGbjczMYA50qqq5Muac8k92OSrpY0TNIXgU8Bp0r6fZXnaCjMdH2apB8nt0mStpB0q6TnJB2dS+cn65E9CIIgCIIgCIIgWPZYYhQMkvYE1gXG4RYIYyXtlJzXBX5lZhvjnfsD0vXLgKPNbFvS2jtmNh84FbjKzMYkSwWADYC9UvinSepfp2jjgG8AmwJrA59M8o0ys03MbNMkRyPPujK+AOPuZrYFvv7D183sN8D1wMlpu8mOniNTOExOiz9uWSvMXLT/Tul0L74Gw4HANrgSozN5j0qKiUkt1FhQKAiCIAiCIAiCIFhqWZKmSOyZfn9P58NwxcJLwAtmNjldfxQYLWkEsJyZZeslXAl8vEb4fzWzecA8SW8AI4H/1PCf8bCZPQ+QrAp2wKdHfFjSL4G/ArfV9YQVtgE2Au5Pu14MACbWee9VZnZsdiJpQp1hXp/+pwDDzGwGvlXn3JSWVTGzS4BLAJbXin1nadYgCIIgCIIgCIJgkbEkKRgEnGVmFxcuSqOhMGzeCgxO/huhHEa9aVPuUJuZvSNpc9wi4qv4tIYjGpBFwO3JSqGn6CzM7PnbKKZFG0tWPgmCIAiCIAiCIAgWA0vMFAngVuCI3LoBoyStWs2zmb2Dj8Bvky59Ouc8A1iuh+QaJ2mttPbCwcB9aTpCk5ldC3wf2KLBMB8Etpe0DoCkIZLW68BfI89Rb5hBEARBEARBEARB0DBLzMi0md0maUNgYjLxnwkcRlpboQpHApdKmgVMAN5L1+8GTklrFJzVTdEmAj/B12D4G/DndHxZUjoAfLuRAM3szbRDxO8lDUyXvwc8W/Ja93M0EGbP0lb99bS++WZVt5n/Wb7nZbHaszds3qJdP0IDBpQuVIxuyrIMemNO4bxp2ivtx6P/59WCW+ucot+eoO29GT0eZmeoublwPmBaJb8ssLai3yf/1X7cst6YgpvNn184f+6wil51zWtLhk4DB7YfDn7gHwWnfE7WgmL8M0cV3+WHr5lNNX60T2VzmvHfXLuqvzLqXyyurWV+FZ8dUCPvW8uC9uPmlVcquLVOr7XpzRJMrlwqZSVswQK6Qk+9j4W85uRRv35V3fo6S5KstahZTzTwXhe6NZd/mt+bVXBbUCPt2ubM7XKcBZoWwXhTU7FMr9U+yNc5NnaDYjCTe7fZ0hFPfWe19uMNvlZsu8zeZp3244E3P1K8UaU6pkYeUb/K0l/60Kii4z+frx5GjTjK9WhXv0P1L9Zx0/dYq/14xO9eL7g1rbRC8eZ5lby94LWi367SNGxo4dxaKwV524xutFfyadmN77nL0efK+PK7al6+2C5uff/9RSLTYqWn3sFieJf10rzKKoXzWn2jMk2DB7Uft80q1htdzcsaUFyCMF83ldvTtejzCgYzG5Y7Ph84vwNvm+T8nJO7PtXMNgOQdAq+sCFm9jawVY04N6nmVpYJmG1m5R0cHqcDqwUzO7yTcHfOHd/VkYz5MKo8x/guhDk6dzw+H0bObTq5dA6CIAiCIAiCIAiCPH1ewdBN9pH0bfw5XwQOX7ziBEEQBEEQBEEQBMHSSZ9Yg0HSzF4IcwwwI23huAnwCPD5Bu5/OL/dY+7XbkNsZhPM7OOl+86UtHsnYf85F95rkv4laa8a/k+XdFIH10dIOiZ3PlrSk/Xc2xUk3dTZjhJBEARBEARBEATBssnSbMEwBtgSuKkrN5vZuC7ed2odfvYHkNRsZrXWkOiMEcAxwK+7EUbdmNneiyKeIAiCIAiCIAiCYMmjT1gw5JF0sqRHJD0h6Yx0bbSkpyVdKmmqpNskDU5uWyW/EyWdLelJSQOAM4GDk5VAtkbCRpImSHpe0vGdyDEz/e8s6W/J6uApSRdJapLULGl8im+KpBOT//GSDqwR7jRJp0q6Dzgo71/S3pKekXSfpAsk3Zi7tSPZfwKsnZ7x7DrSdm1Jt0h6VNK9kjbIyfw/ku5O4X9U0v+mNB9fkn3lDsI9StIkSZNaWLQLJQZBEARBEARBEAR9gz6lYJC0J7AuMA63QBgraafkvC7wKzPbGHgXOCBdvww42sy2JS30bmbzgVOBq9IUiWzZ9g2AvVL4p0kqLpVZnXHAN/DdIdYGPpnkG2Vmm5jZpkmOeplrZjuY2R9yzz4IuBj4LzPbAVildE9Hsp8CPJee8eTkL1M4TE67SxydC+MS4DgzGwucRNHyYQVgV+BE4AbgPGBjYNM03aQqZnaJmW1pZlv2Z2Atr0EQBEEQBEEQBMFSSp9SMAB7pt/fgcfwTvW6ye0FM5ucjh8FRqf1AJYzswfS9Ss7Cf+vZjbPzKYDbwAj65TrYTN7Pk1n+D2wA/A88GFJv5T0MaCR/WKu6uDaBsDzZvZCOv99F2XPFA5jzGwMcBGApGHAdsDVSfFwMbB67r4bzMyAKcDrZjbFzNqAqcDoBp4tCIIgCIIgCIIgWAbpa2swCDjLzC4uXJRGQ8H2vhUYnPw3QjmMep+/vIGomdk7kjbHrQq+CnwKOKLO8GZ1cK2zZ+mq7BlNwLtJ6VAr/LZSXG1diCsIgiAIgiAIgiBYxuhrHcdbgR9IusLMZkoaBbRU85w6+TMkbWNmDwKfzjnPAJbrIbnGSVoL3+ryYOCStBbBfDO7VtJzwPhuxvEMbhEx2sympXg6o+5nNLP3Jb0g6SAzu1qSgM3M7PGui9x7rHvsQ4tbhF6nbfbs+j0/PKVw2p2VQbuCtcxfxDFC6/slo6DyeY62uXPbj5e/8sGa4a535KTqcdYn2kLvY8TD1b2WtZOXrb9m7mxBvTE2ll/KKKe/tKJETYMHtR+3vvV21+NYglC/StVnC0rvoEZa0dRcPG/L5RiVdMTle6vF0YnfmrIuSdRK16DAghf/Xbdfm9cz6x51q3ypO5L6ay5bkGv6PfhEMZiekqcWJVnX+8pjFaeS26A7Ks2ohXJ2A3k9X8+2Pvtc3ffViqOnyoxyG2CF3z+ai74Yf+urrxfvrTePNpA/Wt99r26/DbGYy6Za72uhNlGwVND65ptdvrdtVkfj1Yku5uWeCrNPTZEws9vwaQ4TJU0BrqHzDvSReId/Im4FkJU6d+MLI+YXeewqE/EFFZ8EXgD+DIwCJqTpBuOBb3cnAjObg+8IcUtaAPJ1Ks9S7Z63gPvTQpOdLvIIHAocKelxfOrDft2ROQiCIAiCIAiCIAgyVNY8LmlIGmZm2Y4PpwCrm9nXejD8nYGTzOzjPRVmjbiGJcsNAb8C/mlm5/V2vD3J8lrRttZui1uMIAjy1LJgGDq0/XihUcwlvH6oRlgwLAbCgqF+GslLSyt9Lb/kv/3SSLv6D2g/XhzWfouDWs+sgcXFvnvKyiYIgr7FHXbNo2a2ZUdui8SCIdvysYfDHCNpb2CfZKXwBvA54Ic9HVcNGUZLOqQHg3wrWURMBYbjCzGW4zw+bR95haRPSNoo57bQFpk9lfaS9k0KnCAIgiAIgiAIgiBYiL62BkMjjAG2NLNjgasknQ7MNLO6J7NIWgm4swOn3dL0A8xsAjChShCjgUPoYPcKSdex8O4L3zKzW2uI1FJjEcaMY/CtLF+QNB64EXiqk3u6jZldD1zf2/EEQRAEQRAEQRAESyaLfA0GSSdLekTSE5LOSNdGp1H5SyVNlXSbpMHJbavkd6Kks9N6AwOAM4GDS2ssbCRpgqTnJR3fiRyfw9dpEDAldez3A94C7pZ0p6QPJb/jJV0g6YEUdmYl8BNgxyTDiZIOl3S1pBuA5fHtLN/C1yRqBgZRJ1XS6SLgw8D1kr4L7AucneJfu4thjpb0jKTfpLS9QtLuku6X9E9J45K/wyVdWK/8QRAEQRAEQRAEwbLFIlUwSNoTWBcYh1sgjJW0U3JeF/iVmW0MvAsckK5fBhxtZtuSFnk3s/nAqcBVZjbGzK5KfjfAt40cB5wmqX8VOTYGvgvsamabA9maDRcCvzWzzYArgAtyt62OKww+jisWAE4B7k0yZGslbAt83sx2BeYC+5vZFsAuwLlpfYUupZOZHQ28AuxiZj/CLQpOTvFnSw5nCofJabpFzTCT8zrA+cBmKQ0PSc96EvCdOuQ9StIkSZNaiLl2QRAEQRAEQRAEyyKLeorEnun393Q+DO/0vgS8YGaT0/VHgdGSRgDLmdkD6fqVeAe/Gn81s3nAvLQmw0jgPx342xW4xsymA5hZtjfbtsAn0/HvgJ/l7rnOzNqApySNrCHD7bnwBPw4deTb8J0nRgKv1bgfqqfT3zq5D1zhcE12kluDobO0n5L8TwXuNDNLO3mM7ixCM7sEuAR8kcc6ZAyCIAiCIAiCIAiWMha1gkHAWWZWWLxQ0mgoDH23AoOT/0Yoh1Ht+UQH2xV3QN5PPuxacuU3ED0UWAUYa2YtkqZR3zSJDtOpm9Sb9m258zaW7HU6giAIgiAIgiAIgkXEol6D4VbgCEnDACSNkrRqNc9m9g4wQ9I26dKnc84zgOW6KMedwKfSIo9IWjFdfyAXx6HAfZ2E05kMw4E3knJhF2DNOuWrN50aSYOG0j4IgiAIgiAIgiAIGmGRjk6b2W2SNgQmpqUIZgKHkdZWqMKRwKWSZuG7ObyXrt8NnJLWGTirQTmmSvoRcI+kVnzawOHA8cD/SjoZeBP4QidBPQEskPQ4MB54p+R+BXCDpEnAZOCZOuWrlk5vlLz+AU+b44EDqUEX0z4Igr5CefmWRvaGr+G3sEe5SjpnWzqLB1uwoIZjjXRtq5EePfQ+FvJaS9YliUbSZ1mnkbRqai6e18qjSxD91li9/XjBy68sRkmcfiNXaT9e8NrrBbem9dZqP26d+o9FJlOG+g8onFvL/Ipbv2Izv1CedKNOaV6tMj614N/FmcjWUiyz8vLlZesWZdnzdVd3voH897SUfEvBUkwfzq+yPl7pSxpmZjPT8SnA6mb2tU5uCxYTy2tF21q7LW4xgmDpozsKhlrB5hqg1lYKs49VWEEQlFhaFQyj1mg/7hMKhtVXaz8uKxiaN1qv/XhZUTD0++AH2o/LCoZynlRz5TwUDEHQgyzm/HqHXfOomW3ZkVuPTZHILSbYY0gaA3w37YjwJHAE8Hbtu3pchtGSDunF8G9Ki1nW8jNB0kIvUNIYSXvnzhfaSrLavV2Qcw1J13TuMwiCIAiCIAiCIFgW6esL+I3Bd5EYAyDpdIqLKNYkrbFwZwdOu5nZW3UGMxrftvHKDsLvZ2YN2a9WkWmCpEZkyhgDbAnc1OB9DWNmr9DJNIwgCIIgCIIgCIJg2aVXFnmUdLKkRyQ9IemMdG20pKclXSppqqTbJA1OblslvxMlnS3pSUkDgDOBg5MFw8Ep+I3SqPzzae2BWuyTnlHAlKSo2A/4Y4rvTkkfSjKMl3SBpAdS2Fln+ifAjkmGE5OVwNWSbgBukzQshfOYpCmS9quRLqPxhSMfwHeo2A8YAexuZm9J+r6kZyTdLun3kk7K3X6QpIclPStpxxrpU+u97JnS+LH0DNmCj9Mk/Ti5TZK0haRbJT0n6ejc+3uysziCIAiCIAiCIAiCZZMeVzBI2hNYFxiHj7CPlbRTcl4X+JWZbQy8CxyQrl8GHG1m25IWHTSz+cCpwFVmNsbMrkp+NwD2SuGfJql/FTk2Br4L7GpmmwPZug0XAr81s83wRRgvyN22OrAD8HFcsQBwCnBvkuG8dG1b4PNmtiswF9jfzLYAdgHOlcqTwwqsn+L/iJm9mJN3y5QeHwE+iVsm5OlnZuOAE4DTaqRPpnCYnBbA3DKFvzLwPVyZsQUwCfh6Lvx/p/S/F1+w8kBgG1yJURNJRyXFxKSWwo6XQRAEQRAEQRAEwbJCb0yR2DP9/p7Oh+GKhZeAF8xscrr+KDA6rT+wnJk9kK5fiXfwq/FXM5sHzJP0BjAS+E8H/nYFrjGz6QBmlq3dsC3egQf4HfCz3D3XmVkb8JSkkTVkuD0XnoAfJyVKGzAqyfRalXtfNLMHO7i+A/AXM5sDkCwk8vwp/T+KT9uoxlVmdmx2ImlCOtwG2Ai4P+k/BgATc/ddn/6nAMPMbAa+RejcztaIMLNLgEvAF3ms5TcIgiAIgiAIgiBYOukNBYOAs8zs4sJFnx6QH95uBQYn/41QDqPaMwifhtAZeT/5sGvJlV8H4lBgFWCsmbVImgYMqvPePJ2lQyZbrWeuhXDFyGc6Cb+NYjq0dTG+IAiCIAiCIAiCYBmiN9ZguBU4Ije/f5SkVat5NrN38JHybdKlT+ecZwDLdVGOO4FPpUUVkbRiuv5ALo5D8TURatGZDMOBN5JyYRdgzS7Kex/w35IGpbTbp457GkmfB4HtJa0DIGmIpPU6uScIgiAIgiAIgiAI6qLHR6bN7DZJGwITkyn+TOAw0toKVTgSuFTSLGAC8F66fjdwSlpL4KwG5Zgq6UfAPZJa8SkbhwPHA/8r6WTgTeALnQT1BLBA0uP42gTvlNyvAG6QNAmYDDzTiJw5eR+RdD3wOPAivkbCe7Xvqj99zOxNSYcDv5c0MF3+HvBsV+StSX4Jihp7EzcNGVJxmj274HbrK5ML53utMaanpKvQW/sodzF+DajsZW3zimtZ2LabF/3m9qt+fathBbfVL32s/bht7txuiwnQtFxRj9U2Y0aPhFuL8v7dzR+o7Ive+p/ivuhNH67o9RasUpRVE6cUzvuNXKX9uGWt4kyo5scqe5i3jt2gGM79kysn22xWvO+dYv6d9qmKTvWDP5xYcHvt+G3bj0ddPrXg1vre+4Xzwr7kDexRvhD5vFYKp2nY0Er875aKnBr3Lcnk85a1Fr/1/HdYcw95it9pZ367StOgikFcT33Pi4WlJC/l6y1YuO7qMrn0aV73w8U4nm9frmmhfNU0aGDRb1fl6WPvx2ZVjD0XSvM5c0qee19em5VL13J8L1ebEbtosJb51d1qlUPdSbeWlqpOzSuOKJy3Tm90g7Q6KMtuPdRmWxRtvyBYQlH/AcUL1YuenlMwmNmw3PH5wPkdeNsk5+ec3PWpadFFJJ2Cd66zdRO2qhHnJtXckvvlwOWla9Pw9RnKfg8vnQ9L/y3AbiXv43P+puPrOnRKinuT0rXRudNzzOx0SUOAvwHnJj87l+IbnY47Sp/x+ZPSvXd14L8gg5mNp/h8mdv0suxBEARBEARBEARBkNEr21R2gX3SrgdPAjsCP+xqQJJGSDqm50QrhL1vUoB09f6ZNdxGA/9J1giPAdea2WMd+Ds7bfN5dtoyc42c24S0G0V7mD21taSkoyV9rifCCoIgCIIgCIIgCJY++sTifWmLxas69dgBaY2FO3OXBgBrSbrKzDq1y0pbSirtHtGZnNdT2W2hEZnq5T9mNqYTP18GVjGzeWmHiCeBV2rf0n3M7KLejiMIgiAIgiAIgiBYcukrFgxdxszeMrMx2Q9fMwHgzjTKf7KkRyQ9IekMaB/Zf1rSr3FrgR0lPSPpN5KelHSFpN0l3S/pn5LGpfsOl3RhOh4v6QJJD0h6XtKB6fow4I/47gvNwGk52epCUnOSPZP7y+n69cBQ4CFJBwNbAlck64/BXQxzZ0n3SPqjpGcl/UTSoZIeljRF0trJ3+mSTqr3GYIgCIIgCIIgCIJliz5hwdDDnAJsYmZjJO0JHAiMw7dpvF7STsBLwPrAF8zsmDQ9YR3gIOAo4BHgEGAHYF/gO8AnOohr9eRnA9yy4RpgLrC/mb0vaWXgQUnXmzW0ms6RwHtmtlVakPF+SbeZ2b6SZmbKCklfAU4ys0npHFzhkK2ANABXdFQNM7ltDmwIvA08D/zGzMZJ+hpwHHBCLWElHYWnG4MYUstrEARBEARBEARBsJSyNCoY8uyZfn9P58OAdXEFw4tm9mDO7wtmNgVA0lTgTjMzSVNIiyp2wHVpasVTkrLl6AX8OCky2oBRwEigkWWG9wQ2y6wi8K0w1wVeqOPeQ3MKh9HAjZ2EOR94xMxeTfc8B2SKhynALp1FaGaXAJcALK8VF//y00EQBEEQBEEQBMEiZ2lXMAg4y8wuLlz0jveskt/8voBtufM2qqdT/p5sj6dDgVWAsWbWImkaMKh8Yx1yH2dmtzZ4X8NhStqZrj17EARBEARBEARBELSzxK/B0AEzgOXS8a3AEWldBCSNkrRq1Tt7huHAG0m5sAuwZhfCuBX4iqT+AJLWkzS0A3/5Z+2pMIMgCIIgCIIgCIKgYZa60WkzeystzvgkcDNwJTAxrU8wEzgMaO1FEa4AbpA0CZgMPNOFMH6DT8t4LO1y8SYdrwExHrgorbmwbQ+F2X3yy01Y9aRumz27qtvmPy3uNLoaD3RbrIUoL4tRQ9ZFgbUsqOrW9FgxGzWvuEL78agX3yi4LZg7t2cFA9pmVX9XvYaK+k97b0bluK347uyll9uP56+9acFtYGmDmOm7r9V+3G9eMZzhz1Z0bv2eeangls8dzW8XDaBaRxTXHhn9p8oGNq2lfDbrQxV5Wt8v7VxbY6mWpqFFfWDbrLIRVtdomzWn/Vj9BxTFaZnfI3H0NWxB9W/N5s3r8Lg7YXaHtpwMTYOKxnBtvfCt9xoNLUPUd6lVb3WLXPrkyzMAa61eN9VyWwipcrxQ/df776eh8iVX/mtwyQh0zhx6nXxaAbN22qD9eNBNjxbc5n9k7fbj5rsX2l28fpqaK4elZ65Z3ufuc8+VPNFj9UY5Pbb4UPvxwJteL7i1vv1u9XBqyFrTrSzOwIElv7nvpxv1Vj6PLpb6r8Y32rRccTyxbcYMgiWfhcrFBS1FDzXK5n4jV2k/XvBqIzPxy0Lk8l2p7Z3/Dhupb5Y6BQOAmR1SunR+B942yfmfVjo/vCM3MxuPd+oLftL5sPQ/nSqd/cxPFbd8PG34wpLfqRWGmV0LXJtz3rkLYU5Iv+yenXPH7W5mdno12YMgCIIgCIIgCIJgaZwiEQRBEARBEARBEATBImaJUzBIGiHpmM59dinsfSWd0o37Z9bhZ1NJk9NvVvp/qIb/aWm7y/L1nSVtlzs/XdJJ9dzbKJK2lHRBd8MJgiAIgiAIgiAIll6WxCkSI4BjgF/X4zmtN6A0RaAmZnY9cH23pOs8jimSxpp1e8L/zviaEr2wOEGRtO3lpN6OJwiCIAiCIAiCIFhyWeIsGICfAGunkf+zJZ0s6RFJT0g6A3wbSklPS/o18Biwo6RnJP1G0pOSrpC0e1oM8p+SxqX7Dpd0YToeL+kCSQ9Iel7Sgen6MEl3SnpM0hRJ+9UjdLI4uFvSlcCUdG1m+m+S9GtJUyXdKOmmLL7Ecbn4NkjbbB4NnJjSYcc64j9M0sPJ/8WSmjMZJP1U0qOS7pA0TtKE9Mz75mS/sUbYR0maJGlSC/UvihYEQRAEQRAEQRAsPSyJCoZTgOfMbAxwO7AuMA4YA4yVtFPytz7wWzP7CPAisA6+2ONmwAbAIcAOwEl0sJhiYvXk5+O4YgNgLrC/mW0B7AKcm6wk6mEc8F0z26h0/ZP4Dg+bAl9k4UUip6f4/gc4KS3eeBFwnpmNMbN7k79M4TBZ0mRgDQBJGwIHA9undGsFDk33DAUmmNlYfNvLHwJ7APsDZ9bzUGZ2iZltaWZb9mdg5zcEQRAEQRAEQRAESx1L4hSJPHum39/T+TBc4fAS8KKZPZjz+4KZZZYDU4E7zcwkTcE79x1xXZpa8ZSkkemagB8nRUYbMAoYCdSzP8jDZvZCB9d3AK5Ocb0m6e6S+5/S/6O4MqIa55nZOdmJpGnpcDdgLPBI0oUMBrK9DecDt6TjKcA8M2vpJF2CIAiCIAiCIAiCoMCSrmAQcJaZXVy46FMIypv95m3323LnbVRPh/w9mZXCocAqwNjUEZ8GDCrfWIVqGxB3ZgGRydFK196ZgMvN7NsduLWYtW+y2p4uZtYmaUnPH0EQBEEQBEEQBMEiYkmcIjEDWC4d3wocIWkYgKRRklbt5fiHA28k5cIuwJo9EOZ9wAFpLYaR+AKOnZFPh864EzgwSxtJK0rqCbmDIAiCIAiCIAiCAFgCLRjM7K20OOOTwM3AlcDEZPo/EzgMH+nvLa4AbpA0CZgMPNMDYV6LT2N4EngWeAh4r5N7bgCuSYtMHlfLo5k9Jel7wG2SmoAW4Kv42hQ9T35JCuV0WG3F19I0ZEjFafbsgtvj3ypuErLX+WN6TLx2yktn1JB1UaD+lc/R5hXjb9tig8J5xegEXt9qWMFt9Uvfrdw3d26PyNY0dEjhvG3GjB4JtyaljV80vKJPUyl+fWhU+/GA91uK4aioR135jsospZa1RhbcbGbFyKh1bDHNdf/bFbcVhxbcmt8p5t9pn6roOT/4VDGfDX2pIk/z8sV31/re+0XZc++5bVY1A6g6yIVT01vL/K7HsSRRY9kcNTdXjgcW15SxecVFbG3Bguph1pnmnZLLvz31PS8W8unTU2mzGMjXW7Bw3dVlcumjD65RdHrhpfbjQp6jmF8BaqZsrXRfBO+nofKltVIH2rwFNTz2EqU0GHL31PbjtlL7oP9DlWZgp9uV1SIXbkPlfY32SrfqjTzl9Jj4r/bjcuxNg4tGvQUZarWtGmh3lcvinmKx14E1vr1F0u4KFjndyXMLXn+zh4TI5bsamxyW65tave0lTsEAYGaHlC6d34G3TXL+p5XOD+/IzczGA+PLftL5sPQ/nYUXYSz4qeI2AZhQJcw2SSeZ2UxJKwEPk3aaMLPROf+TSNYNZvYsvmBlxr2UKN17FXBVLZnN7PQq8i0kexAEQRAEQRAEQRDkWSxTJCSNkHRML4W9r6RTunH/zBpua0i6povh/kZSefeIPNPTwor3Aj8ws4UWjZR0UNp+8+60deR2ObfTJZ1U8j9N0spdkbcUzpaSLuhuOEEQBEEQBEEQBMHSy+KyYBgBHAP8uhN/AKRtIJV2WaiJmV0PXN8t6aqH/QpwYC0/kjYFfle6PM/Mtu4k+NeAXZKFRDWOBI4xs7slnY5PCXmgk3C7TbKcmNTb8QRBEARBEARBEARLLotrkcefAGtLmizpbEknS3pE0hOSzgDfCSKN1v8aeAzYUdIzyRLgSUlXSNo9rcfwT0nj0n2HS7owHY+XdIGkByQ9L+nAdH2YpDslPSZpSlrHoFOSTE/m4vmLpFsk/UPSacnb88DL+M4N/fBdLraWNEHSlnXGc5ikh1P6XCypWdKp+HaWF0m6GjgaODH52bErYabrMyX9VNKjku6QNC7J+rykfZOfnSXdWI/sQRAEQRAEQRAEwbLJ4lIwnAI8Z2ZjgNuBdYFxwBhgrKSdkr/1gd+a2UfwBQnXwddb2AzYADgE73SfBHynSlyrJz8fxxUbAHOB/c1sC2AX4NxkJdEo4/BtK8cAByUFwseAV8xsczPbBLilkQAlbQgcDGyf0qcVONTMzsStCA41s4OAi4DzzGyMmWXrL2QKh8mSJgNr1Aoz3TMUmGBmY/GdKX4I7AHsD5xZp8xHSZokaVILvbPwThAEQRAEQRAEQdC36QuLPO6Zfn9P58NwhcNLwItm9mDO7wtmNgVA0lTgTjOztHbB6CrhX5emVjyVtoAEty74cVJktAGjgJH4NIVGuN3M3kry/AlXZNwEnCPpp8CNuc5/vewGjAUeSTqPwcAbdd57npmdk51ImlZHmPOpKEGm4NM5WjpJ0wJmdglwCcDyWnHJXR48CIIgCIIgCIIg6DJ9QcEgfBrBxYWL0migvL9Ofni8LXfeRvVnyd+TWSkcCqwCjE2d6WnAoPKNdVDuTJuZPStpLLA3cJak25L1Qb0IuNzMvt0FeboSZotV9jxsT9O0s0VfyB9BEARBEARBEATBEsDimiIxA8g2tb8VOELSMABJoyStWvXOnmE48EZSLuwCrNnFcPaQtKKkwcAngPslrQHMNrP/A84BtmgwzDuBA7M0SOF3JF8+DXsqzCAIgiAIgiAIgiDoEotlhNrM3kqLMz4J3AxcCUxM5vszgcPwdQJ6iyuAGyRNAiYDz3QxnPvwHSPWAa40s0mS9gLOltQGtABfaSRAM3tK0veA2yQ1pTC+iq9BkecG4Jq0QOVxPRRmz2A5ww6r/hrbZs+u6rb5T4u7mK7WG5tlWMkApYasiwJrWVDVremxYhZtXnGF9uNRLxZn0CyYO7dnBQPaZlV/V72GivpPe29G5bit+O7spZfbj+evvWnBbWBp85npu6/VftxvXjGc4c8Orbg981LBLZ87mt8uGle1jhhSOB/9p7cqbqV8NutDFXla3y/tilvOkznUf0DRa8v8qn4boRBOU3PRsW3xfhO9Ro10tgULOjzuTpjdIpd/mwYVDe3aeuFb7zV6K30WMbXqrW6RSx/79ytFp9bq32Ett67G31s0Uoa1zank7aZhQ4uOc+b0qFwdUlqWa+6OlV3GB9zySMHNNqzUKTw6tetx5srfpgH9C05d/dY1cGDh3OZ1cZ2sUnq0rvuBysnD7xTcan4jteqY8lJojdSH+e+gO/VWXobFUWbViL9pueKYYtuMGQRLPgvl5QUtRQ818mG/VVduP17w2uvdECKX70pt7/z31Eh9s9hM4M3skNKl8zvwtknO/7TS+eEduZnZeGB82U86H5b+pwPbVpFrWA2ZCzLgVhDHlvzciltllO/duVq4yX107vgq4KpaYZjZs/hilxkLrfVQZ5jDcsend+RmZhOACbXkD4IgCIIgCIIgCJZtem2KhKQRko7p3GeXwt5X0induH9mDbc1JF3TxXB/I2mjzn3W7z9ttXlgB9dHSzokd77QVpLV7u0KknrBhCAIgiAIgiAIgiBYWuhNC4YRwDHAr+vxnLaJVNrxoSZmdj1wfbekqx72K5LOSNs85plnZlvn/I0nWUrkrn2xs/AlPQTkbdaulPTZbHeMBhiNb9N5ZYP3dQkz225RxBMEQRAEQRAEQRAsmfTmIo8/AdaWNFnS2ZJOlvSIpCcknQHto/BPS/o18Biwo6Rn0sj+k5KukLR7Wq/hn5LGpfsOl3RhOh4v6QJJD0h6PhuxlzRM0p2SHpM0Ja1V0Clp94rfm9kY4Bf4OgWvASMknZb8DJX0V0mPJzkPTtcnSNqyRtgz8ekT84BjgXeBL5rZFElHSno2hXFp9nyJncrPl9J3x5S+J9bxXGMl3SPpUUm3Slo9J/N5kv6W3sVWkv6U0vuHJdmDIAiCIAiCIAiCoEN604LhFGATMxsjaU/gQGAcvmXi9ZJ2Al4C1ge+YGbHpM79OsBBwFHAI/go/Q7AvsB38N0ayqye/GyAWzZcA8wF9jez9yWtDDwo6frcloz1Mg5fd2E28Iikv+K7TrxiZvsASBpeZ1hDgSfN7NR0H+l/DeD7+I4TM4C7gMc7eb5TgJPM7OMpjJ1JCofcfR8CbpTUH/glsJ+ZvZkUIj8Cjkj+5pvZTpK+BvwFGAu8DTwn6Twze4saSDoKf18MYkgtr0EQBEEQBEEQBMFSyqJa5HHP9Pt7Oh8GrIsrGF40swdzfl/IpgtImgrcaWYmaQo+LaAjrktTK56SNDJdE/DjpMhoA0YBI3FrhEa4PetgS/oT3tG/CThH0k+BG81soQUWq9AKXNvB9XHAPWb2dornamC9nHtHz9cR92YKhxTO+HS4Pq4kuT0pNZqBV3P3ZdNNpgBTzezVdP/zwAeBmgoGM7sEuARgea24dCwVHgRBEARBEARBEDTEolIwCDjLzC4uXHSLhVklv/k9dNpy521Ulzd/T7bXxqHAKsBYM2uRNA0YVL6xDsodZjOzZyWNBfYGzpJ0m5mdWUdYc8063AtRHVzL09HzNYJwxUGHO2dQTONy+i+2nUaCIAiCIAiCIAiCJYfeXINhBpBt2norcISkYQCSRklatRfjBhiObyPZImkXfFpDV9hD0oqSBuPTM+5PUxpmm9n/AefgUxu6w8PARyWtIKkfcEAd9+TTtzP+AawiaVsASf0lbdw1UYMgCIIgCIIgCIJgYXptdNrM3kqLMz4J3IzvdjAxmejPBA7Dpwz0FlcAN0iaBEwGnuliOPcBv8PXhrjSzCZJ2gs4W1Ib0AJ8pTuCmtnLkn4MPAS8AjwFvNfJbU8ACyQ9ju9m8fdqHs1sfloc8oK0XkQ/fAHLqd2RuzdZ7fxlYFfM8nIgHRq3JKd58wrnC15tdKZPN2nrzU+1Y6xlfuG89Z35VXxC29yKfANvfqRmuCN+N7GqW71P2frsc7Xda7it/Y0Ha7hWp5wePUXToIphV9vcuUVH5QymGl6+pg9T67lquTU1F8/z34VKxmU9lF7q178SXfn9BEslbaXyvlZeKtcNfZlGyjBb0NJ+3PrOO70hTicCFNN8wK2Tqvt9/B89E2euPMnXad2hx/JHKT30+LMVp5LXpoEDC+eFcqtWW6KBMrO36sPFXs/ViL9txoxFKEiwqOhOXl7w2us9JEQu39XoizTSF1Djax4uO0g6HNjSzI5dBHENM7OZyYLhz8D/mtmfezvenmZ5rWhba7fFLUYQBHUSCoY+rmDoP6ASZG81qoO+RS/lpSWKvlb21JBH/SpjdbZgwaKSaLGinBKhrMTI1ykQitEgWFq5w6551Mw63D2xN6dIBI1xetoB4kngBeC6xSpNEARBEARBEARBEDRAtxQMkkZIOqanhCmFva+kU7px/8xO3DeVNLn0eyjvx8zG17JekDRG0t4dXH+og7A37UTkC4F+ZraBmR1fbTtNSWdLmpr+D0/rQWRuEyRtmTsfnaaodBtJR0v6XE+EFQRBEARBEARBECx9dHcNhhHAMcCv6/EsX4BBacvFmpjZ9VS2T+xx0laYY7oZzBhgS3zbynzYW0vqZ2a9YSv3ZWAVM5snaQJu8fBKL8RTwMwu6u04giAIgiAIgiAIgiWX7k6R+AmwdhqhP1vSyZIekfSEpDOgfRT9aUm/Bh4DdpT0jKTfSHpS0hWSdk8LQv5T0rh03+GSLkzH4yVdIOkBSc+nBQuRNEzSnZIekzRF0n71Ci7pm+mexyX9JF0bI+nBJP+fJa2Qrk+Q9FNJD0t6VtKOkgYAZwIHp+c/WNLpki6RdBvw2/Ts9yb5HpO0XZ2yNaf0zNLyy+n69cBQ4CFJB+PKjStS/IO7GObOku6R9Mf0bD+RdGh61imS1k7+Tpd0UpWwj5I0SdKkFpacBaeCIAiCIAiCIAiCnqO7FgynAJuY2RhJewIHAuMAAddL2gl4CVgf+IKZHSNpNL4jw0HAUcAjwCHADsC+wHfw7SDLrJ78bIBbNlwDzAX2N7P3Ja0MPCjp+mrTCzIk/VeKY2szmy1pxeT0W+A4M7tH0pnAacAJya2fmY1LUyJOM7PdJZ1KbhFISacDY4EdzGyOpCHAHmY2V9K6wO9xpUBnHAm8Z2ZbSRqIb415m5ntK2mmmY1J8X0FOMnMJqVzcIXDnBTOAKCtVpjJbXNgQ+Bt4HngN+lZvwYcl0uDDjGzS4BLwBd5rOP5giAIgiAIgiAIgqWMntymcs/0y7ZLHAasiysYXjSz/N5sL6QpCkiaCtxpZiZpCjC6SvjXpakVT0kama4J+HFSZLQBo4CRQGd7+O0OXGZmswHM7G359o0jzOye5Ody4OrcPX9K/4/WkBHgejPLOvj9gQsljcF3rluvE7ky9gQ2yyw1gOF4Wr5Qx72H5hQOo4EbOwlzPvCImb2a7nkOyBQPU4Bd6pQ5CIIgCIIgCIIgWIbpSQWDgLPM7OLCRe/kzir5zdvRt+XO22rIlL8n2y/oUGAVYKyZtUiaBgwq31hF1kZH2rP4W2vICMVnPRF4HbcQaMItLupBuCXFrQ3K2HCYknama+8jCIIgCIIgCIIgCNrp7hoMM4Dl0vGtwBGShgFIGiVp1W6G3xnDgTeScmEXYM0677sNl3UIgKQVzew94B1JOyY/nwXuqRZAIv/81eR7NVlefBZoruE3z63AVyT1T/KtJ2loF+LvSphBEARBEARBEARB0DDdGp02s7fS4oxPAjcDVwIT01oAM4HD8BH/3uIK4AZJk4DJwDP13GRmt6RpC5Mkzcd3gfgO8HngoqR4eB74QidB3Q2cImkycFYH7r8GrpV0UPJbtuSoxm/waRiPyRPzTTpel2J8kncOsG0PhRkEQR9E/QcUzq1lfgM3q3JcWqLGWts69teB36WGWs9Vy62tRnXWS2nV0HsOlg5667urUQ70NZqGDGk/bps9u+i4GGRXv/6V6FuL5UDTcpVxntZ33umhCBsoi5tKY1f5cqqWWzdoXmFE+/GC114vRjGvi4t9N/LMZb/13tcZ+fTqobQKgl6jD5fp6mQ9xCBoiOW1om2t3Ra3GEGw1NFbCoZ8uLagpXhf1A9BsPTQhxujZZqGVgws+4SCIV9OlhQMzcOXbz9eVhQM/VYb2X5cVjB0WVEdCoYgaIzFXKbfYdc8amYdbl5Qc4qEpBGSjukNoSTtK+mUbtw/syflqRLHmLRrRG+F/0AdfqalHTLK13fOb3vZ0TaS1e7tgpxbSrqgu+EEQRAEQRAEQRAESy+dTZEYARyDm/p3SjK9V1pzoCZmdj2+3WSvIWlT4Hely/PMbOs6gxiDbyt5Uwdh9zOzBd2VKU2vaESmjJ3xaSidKim6S9qVYlJvxxMEQRAEQRAEQRAsuXS2yONPgLUlTZZ0tqSTJT0i6QlJZ4DvEiHpaUm/Bh4DdpT0jKTfSHpS0hWSdk9rNfxT0rh03+GSLkzH4yVdIOkBSc9nWylKGibpTkmPSZoiab96H0zSN/E1IQTcYmZjgMMBS/L/WdIKye8EST+V9LCkZyXtKGkAcCZwcHr+g5OVwCWSbgN+m5793iTfY3mLgg7k2VnS3cC3gQFJnnXS/7aSfi1pqqQbJd2U204S4LhcGmyQduY4GjgxybZjOb4O4j8sPd9kSRdLak7XZ6Znf1TSHZLGpfR4XtK+OdlvrB1DEARBEARBEARBsCzTmYLhFOC51Am+HVgXGIeP7I+VtFPytz7wWzP7CPAisA5wPrAZsAFwCLADcBK+mGJHrJ78fBxXbIBv67i/mW0B7AKcm6wkaiLpv/AFDLc2s82BnyWn3wLfMrPNgCnAabnb+pnZOOAE4DQzmw+cClxlZmPM7Krkbyywn5kdArwB7JHkOxjobBrBOOC7ZrZR6fon8QUYNwW+yMILNk5PcfwPcJKZTQMuAs5Lst2b/GUKh8nJMmKNlB4bJvm2T++yFd/iE2AoMMHMxuK7UvwQ2APYH1ewdIqkoyRNkjSphS4u7hMEQRAEQRAEQRAs0TSyi8Se6ff3dD4MVzi8BLxoZg/m/L5gZlMAJE0F7jQzkzQF70h3xHVpasVTkrLVYwT8OCky2oBRwEjgtU5k3R24zMxmA5jZ25KGAyPMLNt68nLg6tw9f0r/j9aQEeB6M5uTjvsDF8p3pGgF1utErofN7IUOru8AXJ2e/7Vk6ZAnL9sna4R/npmdk51ImpYOd8MVI48k/cxgXDkCMB+4JR1PwadrtHTyrgqY2SXAJeCLPNZzTxAEQRAEQRAEQbB00YiCQcBZZnZx4aKb65e3X8wPY7flzttqxJm/J7NSOBRYBRibOr3TgEF1ytpoRzeLv7WGjFB81hOB14HNcWuQuZ3EUW2bys6sMuqVrRoCLjezb3fg1mKVrUTa35WZtUnq1jamQRAEQRAEQRAEwbJDZ1MkZgDZBr+3AkdIGgYgaZSkVXtTOGA48EZSLuwCrFnnfbfhsg4BkLSimb0HvJNbr+CzwD3VAkjkn7+afK8my4PPAs01/NbiPuAASU3JemPnOu7pTLY8dwIHZu9L0oqS6k3LIAiCIAiCIAiCIOiUmiPUZvZWWpzxSeBmfNHEicnMfiZwGD6q3ltcAdwgaRIwGXimnpvM7JY0bWGSpPn4LhDfAT4PXJQUD88DX+gkqLuBU9J6Bmd14P5r4FpJByW/1SwUOuNafBrDk8CzwEPAe53ccwNwTVr48rhaHs3sKUnfA26T1AS0AF/F18voUWbwzvQ77JoXgZWB6ely/rh8XsutEb+LO45lUdaeCidkrcfv/G6EYzXc5veCrIsvnJC1b4cTsi5OWWuVA4tO1vr8zuxjstYqJ9/uBVmthlv5vtYafmu5NSJP2e3VGm6d57OO42jkmRvx24hbaw+F01PyLJ44+lo4IWvvlOk9IWv1wWozi18f+AHD0v9KwHPAaotbpm4+z6SOjhtx66lwFkUcy6Ksy+IzL0myLovPHLL27XBC1pB1WXzmJUnWZfGZQ9a+HU7I2rdlrfbrR9BXuFHSCGAA8AMz62whyyAIgiAIgiAIgiDoMyzRCgZJmwK/K12eZ2ZbLw55oOsymdnOvSZUEARBEARBEARBEPQyS7SCwXwrzDGLW448fVGmxcQlVY4bceupcBZFHD0VzpIka0+FE7L27XBC1pC1p8IJWUPWngonZO3b4YSsIWtPhROy9m1ZO0RpLkUQBEEQBEEQBEEQBEGX6WybyiAIgiAIgiAIgiAIgk4JBUMQBEEQBEEQBEEQBN0mFAxBEARBEARBEARBEHSbUDAEQRAEQRAEQRAEQdBtQsEQBEEQBEEQBEEQBEG3+f+7+LvOg3ED/wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "insula_hemisphere_info = {}\n",
    "for i in ['IT','PT']:\n",
    "    insula_hemisphere_info[i] = {}\n",
    "    for j in tmp[i]:\n",
    "        region = Insula_CEA_info[j]['cortex_region']+Insula_CEA_info[j]['cortex_subregion']\n",
    "        if region not in insula_hemisphere_info[i]:\n",
    "            insula_hemisphere_info[i][region] = {'left':[],'right':[]}\n",
    "        hemisphere = 'left' if Insula_CEA_info[j]['soma_position'][2]<5700 else 'right'\n",
    "        insula_hemisphere_info[i][region][hemisphere].append(j)\n",
    "insula_hemisphere_path = data_path+'insula_hemisphere/'\n",
    "create_dir(insula_hemisphere_path)\n",
    "scale = 200\n",
    "sample_info = {}\n",
    "for neuron in Insula_CEA_info:\n",
    "    if neuron[:-7] not in sample_info:\n",
    "        sample_info[neuron[:-7]] = {'neurons':[],'gene':iondata.getSampleInfo(neuron[:-7])[0]['transgenic_line']}\n",
    "    sample_info[neuron[:-7]]['neurons'].append(neuron)\n",
    "for i in insula_hemisphere_info:\n",
    "    for j in insula_hemisphere_info[i]:\n",
    "        try:\n",
    "            X = {'left':[0.0,0.0,0.0,0.0,0.0,0.0],'right':[0.0,0.0,0.0,0.0,0.0,0.0]}\n",
    "            Y = {'left':{},'right':{}}\n",
    "            for k in X:\n",
    "                for l in insula_hemisphere_info[i][j][k]:\n",
    "                    X[k][0] += np.array(Insula_CEA_info[l]['axonal_projection_length_combined'])\n",
    "                    if k=='left':\n",
    "                        X[k][1] += np.array(Insula_CEA_info[l]['axonal_projection_length_left'])\n",
    "                        X[k][2] += np.array(Insula_CEA_info[l]['axonal_projection_length_right'])\n",
    "                    else:\n",
    "                        X[k][2] += np.array(Insula_CEA_info[l]['axonal_projection_length_left'])\n",
    "                        X[k][1] += np.array(Insula_CEA_info[l]['axonal_projection_length_right'])\n",
    "                    X[k][3] += np.array(Insula_CEA_info[l]['axonal_projection_terminal_combined'])*scale\n",
    "                    if k=='left':\n",
    "                        X[k][4] += np.array(Insula_CEA_info[l]['axonal_projection_terminal_left'])*scale\n",
    "                        X[k][5] += np.array(Insula_CEA_info[l]['axonal_projection_terminal_right'])*scale\n",
    "                    else:\n",
    "                        X[k][5] += np.array(Insula_CEA_info[l]['axonal_projection_terminal_left'])*scale\n",
    "                        X[k][4] += np.array(Insula_CEA_info[l]['axonal_projection_terminal_right'])*scale\n",
    "                for l in range(len(X[k])):\n",
    "                    X[k][l] /= len(insula_hemisphere_info[i][j][k])\n",
    "                save_scene(insula_hemisphere_info[i][j][k],color_pool[0 if k=='left' else 3],insula_hemisphere_path+'_'.join([i,j,k]))\n",
    "            X = np.array([[X['left'][k],X['right'][k]] for k in range(len(X['left']))]).reshape(-1,len(brainstem_region))\n",
    "            plt.matshow(X)\n",
    "            x_label = ['length','length_ipsi','length_contra','terminal','terminal_ipsi','terminal_contra']\n",
    "            x_label = np.array([[k+'_leftHemi' for k in x_label],[k+'_rightHemi' for k in x_label]]).transpose(1,0).flatten()\n",
    "            plt.yticks(range(len(x_label)),x_label,fontsize=fontsize_ticks/1.5)\n",
    "            plt.xticks(range(len(brainstem_region)),brainstem_region,fontsize=fontsize_ticks/3.5,rotation=-90)\n",
    "            plt.title('_'.join([i,j])+' ('+str(len(insula_hemisphere_info[i][j]['left']))+' , '+str(len(insula_hemisphere_info[i][j]['right']))+')',fontsize=fontsize_title)\n",
    "            plt.savefig(insula_hemisphere_path+'_'.join([i,j])+'.pdf',format='pdf')\n",
    "            plt.show()\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### insula to CEA neuron distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.pie([len(tmp[i]) for i in ['IT','CT','PT']],labels=['IT','CT','PT'],radius=1,startangle=90,colors=[color_pool[i] for i in [0,3,1]],textprops={'fontsize':30})\n",
    "plt.pie([100],colors=['w'],radius=0.5)\n",
    "plt.title('Insula (CEA,n='+str(np.sum([len(tmp[i]) for i in ['IT','CT','PT']]))+')',fontsize=40)\n",
    "plt.savefig(data_path+'Insula_CEA_IC_CT_PT.pdf',format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index,color,i in zip([0,1],[0,1],['IT','PT']):\n",
    "    X = []\n",
    "    for j in tmp[i]:\n",
    "        X.append(neuron_information[j]['axonal_projection_terminal_combined'])\n",
    "    X = np.mean(X,axis=0)\n",
    "    X = [X[0],np.sum(X[1:-5]),np.sum(X[-5:])]\n",
    "    plt.bar([j+index*0.25 for j in range(len(X))],X,color=color_pool[color],width=0.25,label=i)\n",
    "plt.legend()\n",
    "plt.xticks([j+index*0.25/2 for j in range(len(X))],['lateral','basal','central'],fontsize=20)\n",
    "plt.title('IT and PT comparison',fontsize=30)\n",
    "plt.show()\n",
    "for index,color,i in zip([0,1],[0,1],['IT','PT']):\n",
    "    X = []\n",
    "    for j in tmp[i]:\n",
    "        X.append(neuron_information[j]['axonal_projection_terminal_combined'])\n",
    "    X = np.mean(X,axis=0)\n",
    "    plt.bar([j+index*0.25 for j in range(len(X))],X,color=color_pool[color],width=0.25,label=i)\n",
    "plt.legend()\n",
    "plt.xticks([j+index*0.25/2 for j in range(len(amygdala_region))],amygdala_region)\n",
    "plt.title('IT and PT comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hemisphere information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hemisphere_info = {}\n",
    "for i in tmp:\n",
    "    for j in tmp[i]:\n",
    "        if neuron_information[j]['soma_position'][2]<5700:\n",
    "            hemisphere_info[j] = get_axonal_statics_by_regions(j,amygdala_region,'terminalleftregion')+get_axonal_statics_by_regions(j,amygdala_region,'terminalrightregion')\n",
    "        else:\n",
    "            hemisphere_info[j] = get_axonal_statics_by_regions(j,amygdala_region,'terminalrightregion')+get_axonal_statics_by_regions(j,amygdala_region,'terminalleftregion')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IT [0.07971014 0.24093817 0.2153366 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PT [0.00063331 0.01491146 0.0068928 ]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index,color,i in zip([0,1],[0,3],['IT','PT']):\n",
    "    X = []\n",
    "    for j in tmp[i]:\n",
    "        X.append(hemisphere_info[j])\n",
    "    X = np.mean(X,axis=0).reshape((2,-1))\n",
    "    A = np.array([X[0][0],np.sum(X[0][1:-5]),np.sum(X[0][-5:])])\n",
    "    B = np.array([X[1][0],np.sum(X[1][1:-5]),np.sum(X[1][-5:])])\n",
    "    print(i,B/A)\n",
    "    plt.barh(['lateral','basal','central'],-A,height=0.25,color=color_pool[0],label='ipsi')\n",
    "    plt.barh(['lateral','basal','central'],B,height=0.25,color=color_pool[3],label='contra')\n",
    "    plt.xticks([-13,0,3],[13,0,3])\n",
    "    plt.yticks([0,1,2],['lateral','basal','central'],fontsize=20)\n",
    "    plt.title(i,fontsize=30)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### terminal visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CEA_mask = get_region_mask('CEA')\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "X = [[],[]]\n",
    "Y = []\n",
    "for i,j in zip([0,3],['IT','PT']):\n",
    "    for k in tool_bar(tmp[j]):\n",
    "        neuron_tree = iondata.getNeuronTreeByID(k[:-7],k[-7:])\n",
    "        for terminal in neuron_tree.terminals:\n",
    "            if terminal.type==3:\n",
    "                continue\n",
    "            if if_inside_mask(CEA_mask,np.array(terminal.xyz)/10):\n",
    "                X[0].append(terminal.xyz.copy())\n",
    "                X[1].append(terminal.xyz.copy())\n",
    "                if X[1][-1][2]>5700:\n",
    "                    X[1][-1][2] = 11400-X[1][-1][2]\n",
    "                Y.append(color_pool[i].tolist())\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,X[0],Y)\n",
    "neuronvis.addRegion('CEA',color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_CEA_IT_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[0].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion('CEA',color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_CEA_IT_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[3].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion('CEA',color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_CEA_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C57BL/6J', 'OPRM1-ICREERT2', 'OPRM1', 'SST', 'Pkc-delta-Cre']"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CEA_neuron_gene_info = {}\n",
    "color_buffer = []\n",
    "for i in iondata.getNeuronListBySomaRegion('CEA'):\n",
    "    if len(i['sampleid'])==6 and i['sampleid'][0]!='0' and len(i['name'])==7:\n",
    "        CEA_neuron_gene_info[i['sampleid']+i['name']] = iondata.getSampleInfo(i['sampleid'])[0]['transgenic_line']\n",
    "        if CEA_neuron_gene_info[i['sampleid']+i['name']] not in color_buffer:\n",
    "            color_buffer.append(CEA_neuron_gene_info[i['sampleid']+i['name']])\n",
    "save_scene(list(CEA_neuron_gene_info.keys()),[color_pool[color_buffer.index(CEA_neuron_gene_info[i])].tolist() for i in CEA_neuron_gene_info],data_path+'CEA_gene',region=[],mirror=[],mode=1)\n",
    "for i in color_buffer:\n",
    "    X = []\n",
    "    Y = []\n",
    "    for j in CEA_neuron_gene_info:\n",
    "        if CEA_neuron_gene_info[j] == i:\n",
    "            X.append(j)\n",
    "            Y.append(color_pool[color_buffer.index(i)].tolist())\n",
    "    name = 'C57BL-6J' if i =='C57BL/6J' else i\n",
    "    save_scene(X,Y,data_path+'CEA_gene_'+name,region=[],mirror=[],mode=1)\n",
    "color_buffer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CEA and Insula in PB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "CEA_neuron = []\n",
    "for i in iondata.getNeuronListBySomaRegion('CEA'):\n",
    "    if len(i['sampleid'])==6 and i['sampleid'][0]!='0' and len(i['name'])==7:\n",
    "        CEA_neuron.append(i['sampleid']+i['name'])\n",
    "mask_name = 'PB'\n",
    "mask = get_region_mask(mask_name)\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "X = [[],[]]\n",
    "Y = []\n",
    "for i,j in zip([0,3],[CEA_neuron,tmp['PT']]):\n",
    "    for k in tool_bar(j):\n",
    "        control_console_output(0)\n",
    "        neuron_tree = iondata.getNeuronTreeByID(k[:-7],k[-7:])\n",
    "        control_console_output(1)\n",
    "        for terminal in neuron_tree.terminals:\n",
    "            if terminal.type==3:\n",
    "                continue\n",
    "            if if_inside_mask(mask,np.array(terminal.xyz)/10):\n",
    "                X[0].append(terminal.xyz.copy())\n",
    "                X[1].append(terminal.xyz.copy())\n",
    "                if X[1][-1][2]>5700:\n",
    "                    X[1][-1][2] = 11400-X[1][-1][2]\n",
    "                Y.append(color_pool[i].tolist())\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,X[0],Y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[0].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[3].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading nrrd ... \n",
      "loaded only left brain /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/strcture/structure_651.nrrd\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d77ce59fc454da9b870efd9b5ab7268",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bbb4f302a3b44a14aa9a9f87b3f05fc3",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "CEA_neuron = []\n",
    "for i in iondata.getNeuronListBySomaRegion('CEA'):\n",
    "    if len(i['sampleid'])==6 and i['sampleid'][0]!='0' and len(i['name'])==7:\n",
    "        CEA_neuron.append(i['sampleid']+i['name'])\n",
    "mask_name = 'NTS'\n",
    "mask = get_region_mask(mask_name)\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "X = [[],[]]\n",
    "Y = []\n",
    "for i,j in zip([0,3],[CEA_neuron,tmp['PT']]):\n",
    "    for k in tool_bar(j):\n",
    "        control_console_output(0)\n",
    "        neuron_tree = iondata.getNeuronTreeByID(k[:-7],k[-7:])\n",
    "        control_console_output(1)\n",
    "        for terminal in neuron_tree.terminals:\n",
    "            if terminal.type==3:\n",
    "                continue\n",
    "            if if_inside_mask(mask,np.array(terminal.xyz)/10):\n",
    "                X[0].append(terminal.xyz.copy())\n",
    "                X[1].append(terminal.xyz.copy())\n",
    "                if X[1][-1][2]>5700:\n",
    "                    X[1][-1][2] = 11400-X[1][-1][2]\n",
    "                Y.append(color_pool[i].tolist())\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,X[0],Y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[0].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[3].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading nrrd ... \n",
      "loaded only left brain /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/strcture/structure_852.nrrd\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1138ff0eb704ecf8457fbb6763a716b",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7fe247b325a64255b5fa91554651282e",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "CEA_neuron = []\n",
    "for i in iondata.getNeuronListBySomaRegion('CEA'):\n",
    "    if len(i['sampleid'])==6 and i['sampleid'][0]!='0' and len(i['name'])==7:\n",
    "        CEA_neuron.append(i['sampleid']+i['name'])\n",
    "mask_name = 'PARN'\n",
    "mask = get_region_mask(mask_name)\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "X = [[],[]]\n",
    "Y = []\n",
    "for i,j in zip([0,3],[CEA_neuron,tmp['PT']]):\n",
    "    for k in tool_bar(j):\n",
    "        control_console_output(0)\n",
    "        neuron_tree = iondata.getNeuronTreeByID(k[:-7],k[-7:])\n",
    "        control_console_output(1)\n",
    "        for terminal in neuron_tree.terminals:\n",
    "            if terminal.type==3:\n",
    "                continue\n",
    "            if if_inside_mask(mask,np.array(terminal.xyz)/10):\n",
    "                X[0].append(terminal.xyz.copy())\n",
    "                X[1].append(terminal.xyz.copy())\n",
    "                if X[1][-1][2]>5700:\n",
    "                    X[1][-1][2] = 11400-X[1][-1][2]\n",
    "                Y.append(color_pool[i].tolist())\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,X[0],Y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[0].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[3].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading nrrd ... \n",
      "loaded only left brain /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/strcture/structure_128.nrrd\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f456965166864d609973e725916a3cec",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2dfa1da1a0e749edbea087d23a4de98e",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "CEA_neuron = []\n",
    "for i in iondata.getNeuronListBySomaRegion('CEA'):\n",
    "    if len(i['sampleid'])==6 and i['sampleid'][0]!='0' and len(i['name'])==7:\n",
    "        CEA_neuron.append(i['sampleid']+i['name'])\n",
    "mask_name = 'MRN'\n",
    "mask = get_region_mask(mask_name)\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "X = [[],[]]\n",
    "Y = []\n",
    "for i,j in zip([0,3],[CEA_neuron,tmp['PT']]):\n",
    "    for k in tool_bar(j):\n",
    "        control_console_output(0)\n",
    "        neuron_tree = iondata.getNeuronTreeByID(k[:-7],k[-7:])\n",
    "        control_console_output(1)\n",
    "        for terminal in neuron_tree.terminals:\n",
    "            if terminal.type==3:\n",
    "                continue\n",
    "            if if_inside_mask(mask,np.array(terminal.xyz)/10):\n",
    "                X[0].append(terminal.xyz.copy())\n",
    "                X[1].append(terminal.xyz.copy())\n",
    "                if X[1][-1][2]>5700:\n",
    "                    X[1][-1][2] = 11400-X[1][-1][2]\n",
    "                Y.append(color_pool[i].tolist())\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,X[0],Y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[0].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[3].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading nrrd ... \n",
      "loaded only left brain /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/strcture/structure_1097.nrrd\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f97d101703a7434bb61140050b70db21",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4bed658a4c25406fbab72c01cfc3b029",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "CEA_neuron = []\n",
    "for i in iondata.getNeuronListBySomaRegion('CEA'):\n",
    "    if len(i['sampleid'])==6 and i['sampleid'][0]!='0' and len(i['name'])==7:\n",
    "        CEA_neuron.append(i['sampleid']+i['name'])\n",
    "mask_name = 'HY'\n",
    "mask = get_region_mask(mask_name)\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "X = [[],[]]\n",
    "Y = []\n",
    "for i,j in zip([0,3],[CEA_neuron,tmp['PT']]):\n",
    "    for k in tool_bar(j):\n",
    "        control_console_output(0)\n",
    "        neuron_tree = iondata.getNeuronTreeByID(k[:-7],k[-7:])\n",
    "        control_console_output(1)\n",
    "        for terminal in neuron_tree.terminals:\n",
    "            if terminal.type==3:\n",
    "                continue\n",
    "            if if_inside_mask(mask,np.array(terminal.xyz)/10):\n",
    "                X[0].append(terminal.xyz.copy())\n",
    "                X[1].append(terminal.xyz.copy())\n",
    "                if X[1][-1][2]>5700:\n",
    "                    X[1][-1][2] = 11400-X[1][-1][2]\n",
    "                Y.append(color_pool[i].tolist())\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,X[0],Y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[0].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_CEA_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "x = []\n",
    "y = []\n",
    "for i,j in zip(X[0],Y):\n",
    "    if j==color_pool[3].tolist():\n",
    "        x.append(i)\n",
    "        y.append(j)\n",
    "set_view(neuronvis,'anterior')\n",
    "draw_points(neuronvis,x,y)\n",
    "neuronvis.addRegion(mask_name,color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_'+mask_name+'_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### single neuron visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/220198/199.swc\n",
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/221663/051.swc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "for i,j in zip([0,1],['220198199.swc','221663051.swc']):\n",
    "    neuron_tree = iondata.getNeuronTreeByID(j[:-7],j[-7:])\n",
    "    neuron_tree.somaRadius = 90\n",
    "    neuronvis.addNeuronTree(neuron_tree,somaColor=[0.5,0.5,0.5],dendriteColor=[0.5,0.5,0.5],axonColor=color_pool[i],isLine=False,mirrorToRight=True)\n",
    "set_view(neuronvis,'dorsal')\n",
    "neuronvis.addRegion('CEA',color_pool[7])\n",
    "neuronvis.addRegion('PSTN',color_pool[7])\n",
    "neuronvis.render.savepng(data_path+'Insula_CEA_IT_CT_PT_single_neuron.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/220196/253.swc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/220195/072.swc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/220196/201.swc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/200313/074.swc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n",
      "specified material (mtl1)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "for i,j,k in zip([0,0,1,1],['220196253.swc','220195072.swc','220196201.swc','200313074.swc'],[['CEA'],['CEA'],['CEA','PSTN'],['CEA','NTS']]):\n",
    "    neuronvis.clear()\n",
    "    neuron_tree = iondata.getNeuronTreeByID(j[:-7],j[-7:])\n",
    "    neuron_tree.somaRadius = 90\n",
    "    neuronvis.addNeuronTree(neuron_tree,somaColor=[0.5,0.5,0.5],dendriteColor=[0.5,0.5,0.5],axonColor=color_pool[i],isLine=False,mirrorToRight=True)\n",
    "    for l in k:\n",
    "        neuronvis.addRegion(l,color_pool[7])\n",
    "    set_view(neuronvis,'dorsal')\n",
    "    neuronvis.render.savepng(data_path+'Insula_CEA_IT_CT_PT_single_neuron_'+j[:9]+'.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IT detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_neuron_axon_info(neurons,terminal_or_length='length',if_combined = False,if_log = True,filter=[]):\n",
    "    region_name = brainstem_region\n",
    "    if if_combined:\n",
    "        cortex_neuron_axonal_length_by_region = []\n",
    "        for i in neurons:\n",
    "            cortex_neuron_axonal_length_by_region.append([neuron_to_brainstem_information[i]['axonal_projection_'+terminal_or_length+'_combined']])\n",
    "        cortex_neuron_axonal_length_by_region = np.array(cortex_neuron_axonal_length_by_region).reshape(-1,len(brainstem_region))\n",
    "        for i in brainstem_region_hierachy['FT']:\n",
    "            cortex_neuron_axonal_length_by_region[:,region_name.index(i)] *= 1\n",
    "        for i in brainstem_region_hierachy['SC']:\n",
    "            cortex_neuron_axonal_length_by_region[:,region_name.index(i)] *= 1\n",
    "    else:\n",
    "        cortex_neuron_axonal_length_by_region = []\n",
    "        for i in neurons:\n",
    "            tmp_left = neuron_to_brainstem_information[i]['axonal_projection_'+terminal_or_length+'_left']\n",
    "            tmp_right = neuron_to_brainstem_information[i]['axonal_projection_'+terminal_or_length+'_right']\n",
    "            if neuron_to_brainstem_information[i]['soma_hemisphere']=='left':\n",
    "                cortex_neuron_axonal_length_by_region.append([tmp_left,tmp_right])\n",
    "            else:\n",
    "                cortex_neuron_axonal_length_by_region.append([tmp_right,tmp_left])\n",
    "        cortex_neuron_axonal_length_by_region = np.array(cortex_neuron_axonal_length_by_region).reshape(-1,2*len(region_name))\n",
    "        for i in brainstem_region_hierachy['FT']:\n",
    "            cortex_neuron_axonal_length_by_region[:,region_name.index(i)] *= 1\n",
    "            cortex_neuron_axonal_length_by_region[:,region_name.index(i)+len(region_name)] *= 1\n",
    "        for i in brainstem_region_hierachy['SC']:\n",
    "            cortex_neuron_axonal_length_by_region[:,region_name.index(i)] += cortex_neuron_axonal_length_by_region[:,region_name.index(i)+len(region_name)]\n",
    "            cortex_neuron_axonal_length_by_region[:,region_name.index(i)] *= 1\n",
    "            cortex_neuron_axonal_length_by_region[:,region_name.index(i)+len(region_name)] = 0\n",
    "    if if_log:\n",
    "        cortex_neuron_axonal_length_by_region = np.log(cortex_neuron_axonal_length_by_region+1)\n",
    "    if len(filter)!=0:\n",
    "        tmp = [brainstem_region.index(i) for i in filter]\n",
    "        region_name = [brainstem_region[i] for i in tmp]\n",
    "        if not if_combined:\n",
    "            tmp += [brainstem_region.index(i)+len(brainstem_region) for i in filter]\n",
    "        cortex_neuron_axonal_length_by_region = np.array([cortex_neuron_axonal_length_by_region[:,i] for i in tmp]).T\n",
    "    return [region_name,cortex_neuron_axonal_length_by_region]\n",
    "def cluster_neuron(neurons,regions,feature,threhold,save_path,regeneration=False,circle_size=15):\n",
    "    fig = plt.figure(figsize=(10,5))\n",
    "    cortex_cluster_object = hierarchy.dendrogram(hierarchy.linkage(feature,'ward'),color_threshold=threhold,distance_sort=True)\n",
    "    plt.axhline(threhold, linestyle='dashed', color='#AAAAAA')\n",
    "    plt.title(\"Hierachy of the clustering\",fontsize=fontsize_title)\n",
    "    plt.locator_params(axis='y', nbins=3)\n",
    "    plt.xticks([],[])\n",
    "    plt.xlabel('neurons = '+str(len(neurons)),fontsize=fontsize_label)\n",
    "    plt.ylabel('Cluster distance',fontsize=fontsize_label,rotation=-90).set_verticalalignment('top')\n",
    "    plt.savefig(save_path+'cluster_hierachy.pdf',format='pdf')\n",
    "    plt.show()\n",
    "        \n",
    "    cluster_object_parse = [[],[]]\n",
    "    neuron_to_cluster = {}\n",
    "    for i,j in zip(cortex_cluster_object['leaves'],cortex_cluster_object['leaves_color_list']):\n",
    "        neuron = neurons[i]\n",
    "        projection_matrix = feature[i]\n",
    "        cluster_object_parse[0].append(color_pool_hex.index(j))\n",
    "        cluster_object_parse[1].append(projection_matrix)\n",
    "        neuron_to_cluster[neuron] = color_pool_hex.index(j)\n",
    "    cluster_object_parse[1] = np.array(cluster_object_parse[1])\n",
    "    cluster_object_parse_visualization = np.zeros(shape=(len(neurons),1+len(regions),3))\n",
    "    cluster_object_parse_visualization[:,0,:] = np.array([color_pool[i] for i in cluster_object_parse[0]])\n",
    "    tmp = np.array([cluster_object_parse[1][:,:len(regions)],cluster_object_parse[1][:,:len(regions)]*0,cluster_object_parse[1][:,len(regions):]]).transpose((1,2,0))\n",
    "    tmp /= np.max(tmp)\n",
    "    cluster_object_parse_visualization[:,1:,:] = tmp*1\n",
    "    fig = plt.figure(figsize=(4*2*2.25,12*2))\n",
    "    ax = fig.add_subplot()\n",
    "    ax.matshow(cluster_object_parse_visualization.transpose((1,0,2)))\n",
    "    _ = ax.set_xticks([],[])\n",
    "    ax.set_xlabel('neurons = '+str(len(neurons)),fontsize=fontsize_label)\n",
    "    cluster_object_parse_visualization_yticks = [[0],['class']]\n",
    "    cluster_object_parse_visualization_yticks[0] += [1+i for i in range(len(regions))]\n",
    "    cluster_object_parse_visualization_yticks[1] += [i if i!='unknow' else 'Spine' for i in regions]\n",
    "    _ = ax.set_yticks(cluster_object_parse_visualization_yticks[0],cluster_object_parse_visualization_yticks[1],fontsize=fontsize_ticks/2)\n",
    "    plt.savefig(save_path+'cluster_projection_matrix.pdf',format='pdf')\n",
    "    plt.show()\n",
    "\n",
    "    cluster_info = {}\n",
    "    for i,j in enumerate(neurons):\n",
    "        _cluster = color_pool_hex.index(cortex_cluster_object['leaves_color_list'][cortex_cluster_object['leaves'].index(i)])\n",
    "        if _cluster not in cluster_info:\n",
    "            cluster_info[_cluster] = []\n",
    "        cluster_info[_cluster].append([j,[0,0]])\n",
    "\n",
    "    for i in range(len(list(cluster_info.keys()))):\n",
    "        tmp3 = []\n",
    "        for j in cluster_info[i]:\n",
    "            tmp3.append(j[0])\n",
    "        save_scene(tmp3,color_pool[i],save_path+'cluster_'+str(i+1))\n",
    "    np.save(save_path+'cluster_info_dict.npy',cluster_info)\n",
    "    cluster_info_transform = {}\n",
    "    for i in cluster_info:\n",
    "        for j in cluster_info[i]:\n",
    "            cluster_info_transform[j[0]] = i\n",
    "    np.save(save_path+'cluster_info_transform_dict.npy',cluster_info_transform)\n",
    "    return cluster_info_transform\n",
    "fontsize_title = 20\n",
    "fontsize_label = 15\n",
    "fontsize_ticks = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x1728 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_name = ['Isocortex','OLF','CTXsp','STRd','ACB','sAMY']\n",
    "projection_length = []\n",
    "for i in tmp['IT']:\n",
    "    if neuron_information[i]['soma_position'][2]<5700:\n",
    "        projection_length.append(get_axonal_statics_by_regions(i,region_name,'terminalleftregion')+get_axonal_statics_by_regions(i,region_name,'terminalrightregion'))\n",
    "    else:\n",
    "        projection_length.append(get_axonal_statics_by_regions(i,region_name,'terminalrightregion')+get_axonal_statics_by_regions(i,region_name,'terminalleftregion'))\n",
    "projection_length = np.array(projection_length).astype('float')\n",
    "projection_length[:,0] /= 1\n",
    "projection_length[:,len(region_name)] /= 1\n",
    "projection_length[:,region_name.index('STRd')] /= 1\n",
    "projection_length[:,len(region_name)+region_name.index('STRd')] /= 1\n",
    "projection_length /= np.linalg.norm(projection_length,axis=-1).reshape(-1,1)\n",
    "cluster_info_transform = cluster_neuron(tmp['IT'],region_name,projection_length,2.5,data_path,circle_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19183/2935453110.py:26: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.yticks([],[])\n",
      "/tmp/ipykernel_19183/2935453110.py:26: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.yticks([],[])\n",
      "/tmp/ipykernel_19183/2935453110.py:26: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.yticks([],[])\n",
      "/tmp/ipykernel_19183/2935453110.py:26: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead.\n",
      "  plt.yticks([],[])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_info_transform = {}\n",
    "for i in range(5):\n",
    "    for j in Scene.scene2List(data_path+'cluster_'+str(i+1)+'.nv'):\n",
    "        cluster_info_transform[j['sampleid']+j['name']] = i\n",
    "compared_region = ['Isocortex','OLF','CTXsp','STRd','ACB','sAMY']\n",
    "compared_region_label = compared_region\n",
    "cluster_sequence = [2,1,4,3,5]\n",
    "tmp = {}\n",
    "for i in cluster_sequence:\n",
    "    tmp2 = []\n",
    "    for j in Scene.scene2List(data_path+'cluster_'+str(i)+'.nv'):\n",
    "        neuron = j['sampleid']+j['name']\n",
    "        tmp2.append(neuron)\n",
    "    if 0:\n",
    "        a,b = get_neuron_axon_info(tmp2,terminal_or_length='terminal',if_combined = True,if_log = False,filter=compared_region)\n",
    "    else:\n",
    "        b = []\n",
    "        for j in tmp2:\n",
    "            b.append(get_axonal_statics_by_regions(j,compared_region,'terminalregion'))\n",
    "    tmp[i] = [len(tmp2),np.mean(b,axis=0)]\n",
    "for figure_index,i in enumerate(cluster_sequence):\n",
    "    plt.subplot(1,5,figure_index+1)\n",
    "    plt.barh(compared_region_label[::-1],tmp[i][1][::-1],color=color_pool[i-1],label='cluster_'+str(i))\n",
    "    plt.xticks([int(max(tmp[i][1]))],[int(max(tmp[i][1]))])\n",
    "    if figure_index!=0:\n",
    "        plt.yticks([],[])\n",
    "    plt.title(str(i),fontsize=fontsize_label*0.7)\n",
    "plt.suptitle('Average terminal',fontsize=fontsize_label)\n",
    "#plt.savefig(part_path+'cluster_difference.pdf',format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in cluster_sequence:\n",
    "    tmp2 = []\n",
    "    for j in Scene.scene2List(data_path+'cluster_'+str(i)+'.nv'):\n",
    "        neuron = j['sampleid']+j['name']\n",
    "        tmp2.append(neuron)\n",
    "    if 0:\n",
    "        a,b = get_neuron_axon_info(tmp2,terminal_or_length='terminal',if_combined = True,if_log = False,filter=compared_region)\n",
    "    else:\n",
    "        b = []\n",
    "        for j in tmp2:\n",
    "            b.append(get_axonal_statics_by_regions(j,['STRd','ACB','sAMY'],'terminalregion'))\n",
    "    tmp[i] = [len(tmp2),np.mean(b,axis=0)]\n",
    "    plt.figure(figsize=(5,5))\n",
    "    plt.pie(tmp[i][1],labels=['STRd','ACB','sAMY'],radius=1,startangle=90,colors=[color_pool[i] for i in [3,1,0]],textprops={'fontsize':30})\n",
    "    plt.pie([100],colors=['w'],radius=0.5)\n",
    "    plt.title('cluster '+str(i)+' (n='+str(tmp[i][0])+')',fontsize=40)\n",
    "    #plt.savefig(data_path+'Insula_CEA_IC_CT_PT.pdf',format='pdf')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ORB, IT, CT, PT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "173a873d5b924055b5e65b855c622524",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generating neuron information...:   0%|          | 0/533 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ORB_path = data_path+'ORB/'\n",
    "create_dir(ORB_path)\n",
    "cortex_region = ['FRP','PL','ILA','ORB','ACA','AI','GU','VISC','PTLp','RSP','TEa','ECT','PERI','MOs','MOp','SSp','SSs','AUD','VIS']\n",
    "# to facilidate using, also adding extra information\n",
    "brainstem_region_hierachy = {\n",
    "    'Cortex':['FRP','PL','ILA','ORB','ACA','AI','GU','VISC','PTLp','RSP','TEa','ECT','PERI','MOs','MOp','SSp','SSs','AUD','VIS'],\n",
    "    'OLF':['MOB','AOB','AON','TT','DP','PIR','NLOT','COA','PAA','TR'],\n",
    "    'HPF':['CA','DG','FC','IG','ENT','PAR','POST','PRE','SUB','ProS','HATA','APr'],\n",
    "    'Cortex-sp':['CLA','EP','LA','BLA','BMA','PA'],\n",
    "    'CNU':['STRd','ACB','FS','OT','LSX','AAA','CEA','MEA','PALd','PALv','PALm','PALc'],\n",
    "    'TH':['VAL','VM','VPL','VPM','VPLpc','VPMpc','PoT','SPA','SPFm','SPFp','PP','MG','LGd','LP','PO','POL','SGN','Eth','AV','AM','AD','IAM','IAD','LD',\n",
    "    'IMD','MD','SMT','PR','PVT','PT','RE','Xi','RH','CM','PCN','CL','PF','PIL','GENv','EPI','RT'],\n",
    "    'HY':['SO','ASO','PVH','PVa','PVi','ARH','ADP','AVP','AVPV','DMH','MEPO','MPO','OV','PD','PS','PVp','PVpo','SBPV','SCH','SFO','VMPO','VLPO','AHN',\n",
    "    'MBO','MPN','PMd','PMv','PVHd','VMH','PH','LHA','LPO','PST','PSTN','PeF','RCH','STN','TU','ZI','ME'],\n",
    "    'MB':['APN','NOT','NPC','PPT','RPF','AT','MPT','OP','VTA','SNr','SNc','SCs','SCm','NB','SAG','PBG','IC','RR','MRN','RN',\n",
    "    'PAG','PRC','INC','ND','Su3','CUN','PPN','IF','IPN','RL','CLI','DR','III','MA3','EW','VTN'],\n",
    "    'P':['PSV','PB','PCG','PG','PRNr','PRNc','SUT','TRN','V','P5','CS','LDT','NI','RPO','SOC','NLL','DTN','PDTg','SG','I5','SLC','SLD'],\n",
    "    'MY':['DCO','VCO','LAV','MV','SPIV','SUV','DMX','GR','CU','ECU','NTS','NTB','SPVC','SPVI','SPVO','Pa5','VII','GRN','IRN','LIN','LRNm','LRNp','MARN',\n",
    "    'MDRNd','MDRNv','PARN','PGRNd','PGRNl','PHY','VI','AMB','ICB','PAS','PRP','PPY','x','XII','y','IO','RM','RPA','RO'],\n",
    "    'CB':['VERM','HEM','FN','IP','DN','VeCB'],\n",
    "    'FT':['py','pyd'],\n",
    "    'SC':['unknow']\n",
    "}\n",
    "cortex_region_to_subregion,cortex_subregion_to_region = get_subregions(cortex_region)\n",
    "type = {}\n",
    "for i in iondata.getNeuronListBySomaRegion('ORB'):\n",
    "    type[i['sampleid']+i['name']] = i['type']\n",
    "ORB_CEA_info = {}\n",
    "for i in tool_bar(neuron_source[source_region.index('Isocortex')],desc='generating neuron information...',leave=False):\n",
    "    ORB_CEA_info[i] = {}\n",
    "    neuron_property = iondata.getNeuronPropertyByID(i[:-7],i[-7:])\n",
    "    ORB_CEA_info[i]['soma_position'] = neuron_property['somapoint']\n",
    "    ORB_CEA_info[i]['soma_hemisphere'] = 'left' if neuron_property['somapoint'][2]<5700 else 'right'\n",
    "    a,b = region_layer_seperate(neuron_property['somaregion'])\n",
    "    ORB_CEA_info[i]['cortex_region'] = cortex_subregion_to_region[neuron_property['somaregion']]\n",
    "    ORB_CEA_info[i]['cortex_subregion'] = a[len(ORB_CEA_info[i]['cortex_region']):]\n",
    "    if i in type:\n",
    "        ORB_CEA_info[i]['type'] = type[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = {}\n",
    "for i in ORB_CEA_info:\n",
    "    try:\n",
    "        if ORB_CEA_info[i]['type'][:2] not in tmp:\n",
    "            tmp[ORB_CEA_info[i]['type'][:2]] = []\n",
    "        tmp[ORB_CEA_info[i]['type'][:2]].append(i)\n",
    "    except:\n",
    "        pass\n",
    "for i in tmp:\n",
    "    save_scene(tmp[i],color_pool[0 if i=='IT' else 3 if i=='CT' else 1 if i=='PT' else 2],ORB_path+i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'IT'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb Cell 26\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#Y120sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m5\u001b[39m,\u001b[39m5\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#Y120sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mpie([\u001b[39mlen\u001b[39m(tmp[i]) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mIT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mCT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mPT\u001b[39m\u001b[39m'\u001b[39m]],labels\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mIT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mCT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mPT\u001b[39m\u001b[39m'\u001b[39m],radius\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,startangle\u001b[39m=\u001b[39m\u001b[39m90\u001b[39m,colors\u001b[39m=\u001b[39m[color_pool[i] \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m0\u001b[39m,\u001b[39m3\u001b[39m,\u001b[39m1\u001b[39m]],textprops\u001b[39m=\u001b[39m{\u001b[39m'\u001b[39m\u001b[39mfontsize\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m30\u001b[39m})\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#Y120sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mpie([\u001b[39m100\u001b[39m],colors\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mw\u001b[39m\u001b[39m'\u001b[39m],radius\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#Y120sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m'\u001b[39m\u001b[39mInsula (CEA,n=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m+\u001b[39m\u001b[39mstr\u001b[39m(np\u001b[39m.\u001b[39msum([\u001b[39mlen\u001b[39m(tmp[i]) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mIT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mCT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mPT\u001b[39m\u001b[39m'\u001b[39m]]))\u001b[39m+\u001b[39m\u001b[39m'\u001b[39m\u001b[39m)\u001b[39m\u001b[39m'\u001b[39m,fontsize\u001b[39m=\u001b[39m\u001b[39m40\u001b[39m)\n",
      "\u001b[1;32m/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb Cell 26\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#Y120sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m5\u001b[39m,\u001b[39m5\u001b[39m))\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#Y120sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mpie([\u001b[39mlen\u001b[39m(tmp[i]) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mIT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mCT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mPT\u001b[39m\u001b[39m'\u001b[39m]],labels\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mIT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mCT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mPT\u001b[39m\u001b[39m'\u001b[39m],radius\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,startangle\u001b[39m=\u001b[39m\u001b[39m90\u001b[39m,colors\u001b[39m=\u001b[39m[color_pool[i] \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m0\u001b[39m,\u001b[39m3\u001b[39m,\u001b[39m1\u001b[39m]],textprops\u001b[39m=\u001b[39m{\u001b[39m'\u001b[39m\u001b[39mfontsize\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m30\u001b[39m})\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#Y120sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mpie([\u001b[39m100\u001b[39m],colors\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mw\u001b[39m\u001b[39m'\u001b[39m],radius\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#Y120sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m'\u001b[39m\u001b[39mInsula (CEA,n=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m+\u001b[39m\u001b[39mstr\u001b[39m(np\u001b[39m.\u001b[39msum([\u001b[39mlen\u001b[39m(tmp[i]) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m'\u001b[39m\u001b[39mIT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mCT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mPT\u001b[39m\u001b[39m'\u001b[39m]]))\u001b[39m+\u001b[39m\u001b[39m'\u001b[39m\u001b[39m)\u001b[39m\u001b[39m'\u001b[39m,fontsize\u001b[39m=\u001b[39m\u001b[39m40\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'IT'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.pie([len(tmp[i]) for i in ['IT','CT','PT']],labels=['IT','CT','PT'],radius=1,startangle=90,colors=[color_pool[i] for i in [0,3,1]],textprops={'fontsize':30})\n",
    "plt.pie([100],colors=['w'],radius=0.5)\n",
    "plt.title('Insula (CEA,n='+str(np.sum([len(tmp[i]) for i in ['IT','CT','PT']]))+')',fontsize=40)\n",
    "plt.savefig(ORB_path+'Insula_CEA_IC_CT_PT.pdf',format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea84bfc8883744838f406e7dcadfed05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/192108/078.swc\n",
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/194784/052.swc\n",
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/195820/012.swc\n",
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/195820/021.swc\n",
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/196549/008.swc\n",
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/200297/064.swc\n",
      "exist  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/192108/084.swc\n",
      "write  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/192108/232.swc\n",
      "write  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/194782/035.swc\n",
      "write  /home/dell/CX/cx_project/neuron-vis/project_amygdala/../neuronVis/../resource/swc/195819/006.swc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "specified material (mtl1)  not loaded!\n"
     ]
    }
   ],
   "source": [
    "#CEA_mask = get_region_mask('CEA')\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)\n",
    "X = [[],[]]\n",
    "Y = []\n",
    "for i,j in zip([3],['PT']):\n",
    "    for k in tool_bar(tmp[j]):\n",
    "        neuron_tree = iondata.getNeuronTreeByID(k[:-7],k[-7:])\n",
    "        for terminal in neuron_tree.terminals:\n",
    "            if terminal.type==3:\n",
    "                continue\n",
    "            if if_inside_mask(CEA_mask,np.array(terminal.xyz)/10):\n",
    "                X[0].append(terminal.xyz.copy())\n",
    "                X[1].append(terminal.xyz.copy())\n",
    "                if X[1][-1][2]>5700:\n",
    "                    X[1][-1][2] = 11400-X[1][-1][2]\n",
    "                Y.append(color_pool[i].tolist())\n",
    "set_view(neuronvis,'dorsal')\n",
    "draw_points(neuronvis,X[0],Y)\n",
    "neuronvis.addRegion('CEA',color_pool[7])\n",
    "neuronvis.render.savepng(ORB_path+'CEA_IT_PT_terminal.png')\n",
    "neuronvis.render.closeWindow()\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "neuronvis.clear(root=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AMY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bef07e29c8b54df08df86ea731e92900",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "iteration on neurons (generating shaft) ...:   0%|          | 0/24575 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "terminal_thredhold = 5\n",
    "AMY = []\n",
    "for i in tool_bar(neuron_information,desc='iteration on neurons (generating shaft) ...',leave=False,colour='#55AF7B'):\n",
    "    if np.sum(neuron_information[i]['axonal_projection_terminal_combined'])<terminal_thredhold:\n",
    "        continue\n",
    "    AMY.append(i)\n",
    "save_scene(AMY,[1,0,0],data_path+'AMY_th_'+str(terminal_thredhold))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_region = ['Isocortex','OLF','HPF','CNU','HY','TH','MB','P','MY']\n",
    "if 0:\n",
    "    mask = 0\n",
    "    for i,j in enumerate(source_region):\n",
    "        mask = np.where(get_region_mask(j)>0,i+1,mask)\n",
    "source_region = ['unknow']+source_region\n",
    "neuron_source = {}\n",
    "for i in AMY:\n",
    "    region = int(get_mask_value(mask,np.array(neuron_information[i]['soma_position'])/10))\n",
    "    if region not in neuron_source:\n",
    "        neuron_source[region] = []\n",
    "    neuron_source[region].append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Neuron distribution (n=7594)')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_label = source_region\n",
    "X = []\n",
    "for i in range(len(x_label)):\n",
    "    try:\n",
    "        X.append(len(neuron_source[i]))\n",
    "    except:\n",
    "        X.append(0)\n",
    "plt.bar(range(len(x_label)),X)\n",
    "plt.xticks(range(len(x_label)),x_label,rotation=-90)\n",
    "plt.ylabel('number')\n",
    "plt.title('Neuron distribution (n='+str(np.sum(X))+')')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PLgZeA3w+ye1D21uAZ0Lf+fdWeklqaiteQpGkTcEAl6SmDHBJasoAl6SmDHBJasoAl6SmDHBJaur/AWYHgVGOEtRUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HvXMmcCnwTJJd3bJrgROh3ePvR+klqVFr8RSKJE0EAy5JjTLgktQoAy5JjTLgktQoAy5JjTLgktSo/wUK1MZ7qKHCoQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C4Cjwy91vpa8DHqiqxc0edEyGbn+SZydJd/1aBv+d3L/pk26+Sd7vQ03yfu+26+3Aqap603lWa27/b7tTKHWerwBI8mvd/W8GPsTgN9JfBL4N3LxV845az+3/eeDXkzwCfAd4eXW/pm9ZktsYvNPikiRngD8ELoTJ3+/Qa/sncr93rgdeCXw+yV3dstcDPwDt7n8/Si9JjdqOp1AkaSIYcElqlAGXpEYZcElqlAGXpEYZcElqlAGXpEb9DwKNv2E/U5NwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "KeyError",
     "evalue": "9",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb Cell 16\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#X42sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m source_region:\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#X42sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m     X \u001b[39m=\u001b[39m []\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#X42sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m     \u001b[39mfor\u001b[39;00m j \u001b[39min\u001b[39;00m neuron_source[source_region\u001b[39m.\u001b[39;49mindex(i)]:\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#X42sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m         X\u001b[39m.\u001b[39mappend(neuron_information[j][\u001b[39m'\u001b[39m\u001b[39maxonal_projection_terminal_combined\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/dell/CX/cx_project/neuron-vis/project_amygdala/cortex_to_amygdala.ipynb#X42sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     X \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mmean(X,axis\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n",
      "\u001b[0;31mKeyError\u001b[0m: 9"
     ]
    }
   ],
   "source": [
    "for i in source_region:\n",
    "    X = []\n",
    "    for j in neuron_source[source_region.index(i)]:\n",
    "        X.append(neuron_information[j]['axonal_projection_terminal_combined'])\n",
    "    X = np.mean(X,axis=0)\n",
    "    X = [X[0],np.sum(X[1:-5]),np.sum(X[-5:])]\n",
    "    plt.bar([0,1,2],X,color=color_pool[source_region.index(i)],width=0.4)\n",
    "    plt.title(i,fontsize=20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fg3K8/7JtE+yYDfUPQ+sHuX1su9YBownHm2wHyTW/FMElwG22Hn/vOK/dch0Ti9vI+l74kuMn0ucHp1BSOT7bD9U1k4DGObD9d9D4iu00uXIe4fh9tkPkmueLoLI6Ug6sBips5ui3nUV3Xc1+fXdm5+3FXseMo9+VZ9Hr8LHZuPuea3wdtv4Smt+2nSTb3gcOIBxvsx0kl/zw9uF5WC4BgO39mHDubXy0YQjrMnm/RWP3ZeCfr2HQ4zd5twQASo+F4VEY/AcoGmU7TTaNAk6zHSLXPL1GUFkdKQI+ALK0hyx9kmDTtTPYMvY9DuzRHRUV0vei0+l70elIr+IMpcuRRAPU3Q7b7wUSttNkw0LC8cNsh8glr68RfB0PlQCAKWDozy8nPOcY3uzufRQdtB+Dn72Dfpee6b8SACgog4FXw7CnoGi07TTZcCg1oRNth8glrxfBdNsBOiT0mfldDvvzVNLeg9b7O//D4GfvoPiQAKxelxwBw5+D3lNtJ8mGvBrWzLObBpXVkUlAzHaOrkxYyss//TWfl66mVCsqpP/159LnrK/kKFmObfstbL2BgB1/cDTh+Bu2Q+SCl9cIfmw7QCoWjeO46dczt6WIPb73LAP7UfHo9cEtAYD+58OQP4Jk/DQQm86xHSBXPLlGUFkdGQLE8dHsxX13sPiuq9i33w4GJF9fMGQAFY/dSPHYfS0ly7GmxbD5m5DYajtJJtQBwwnHG20HyTavrhGcjo9KAKC+D+PPncHHtUP5sP26giEDGPT4TflTAgAl42HoLCgYYDtJJpQDJ9kOkQteLYJv2Q7QHa3FfObH11O6fAzLpaI/FY/dSNHonB8VbV+vg2HII0HZTPDlazFdnts0qKyOjALes52jJwop3PnEgbe3VXxmdD/bWaxqeBE2fxuf70BsAUYQjm+xHSSbvLhG8P9sB+ipS8b9sHfelwBA2RdgwM9sp+ipYpxN1UDzYhGcaTtAT0wdGeGkcF4di9K5/udBH98fsRv4zQNPbRpUVkcOBP9OZz62fAy/PXYGRQW+2s+ZfaYRNnwZWlbZTtITIwnH19oOkS1eWyP4ku0A3VVcUMRV46drCXRESqHiV3jv5ZaWE2wHyCav/WZ8WwTfG3MmI/uFbcfwrpJDof8PbafoiS/YDpBNnimCyupIL8j9SECZMLZ8DGd85mu2Y3hf+SVQNMZ2iu6aYjtANnmmCIDPAX1sh+iOCw76PkUFhbZjeJ+UOGct+tNwakI9O/Xcw7xUBL7cLDh26ETGV2R/YOXAKDsBSjIySZUNgV0r8FIR+O6HLAjnHKCT6qZtgG/XCgK7n8ATReCOROSB0TrT88XQ5xnVf6TtGP5TcgSUHm87RXdMpibU+enmPuWJIgDGAqW2Q6TrG/udYjuCf/X35Rm+g8jFBDsWeKUIfDc+3MEDxjJ2gG/3gNtXOgmKfTOrXDItgiw61HaAdH01/GXbEfyvry+PJg/kOwdeKQJfrRGUFpYwecQk2zH8r/dUfDbsBDibsYHjlSKYYDtAOiYOPoyyIt/t0vCewoFQcpTtFOnSIsiGyurIcCDN6XztmjTMt++De0+Z7w4f2Z+akPW/m0zzwhPaz3aAdAjCMUMn2o4RHGW+O5enDAjc2HNaBGk6cMAYBpYMsB0jOIpH+nGSlMBtHnihCHx1yt6BA/a3HSF4SibYTpCulN/3FJHhIvIXEXlfRJaLyLMikvaLSESmiUioG7e7RkQu7Wo5LxTBXrYDpOOA/r777+V9vT5rO0G6hqeykIgI8CQwxxgzyhhzEHAlMKwbjzkN6LAIRKTHZ7x54b2btFvOpv3LtQgyrth3RTA4xeWmAC3GmHvbrzDGLAIQkctwxkIsAZ40xvxCREYCfwdeBY4F1gNfBaqAI4CHRaQBOAZYATyAc7Le3SLSD2dCll44g/9+2xizM9Un5IU1gu60oxW9CorZt28eDk+ebb18d7BeqkVwCLBg9ytF5EvAGOBInLfODxeRz7vfHgPcY4w5GNgKTDXGzAbeAs40xkwwxjS4yzYaYyYZY/4CPGGMmWiMGY9TEmen84S8sEbgm8Hvh5QO1nEHsqGgDxRUQOJj20lSNaiHt/+S+7HQ/bovTgHUAB+0rzXglMjITu7nsaTLh4jIDcAA9/6eSyeQF4qgt+0AqRpcWmE7QnAVDvdTEaR63MsyoKMhnAW4yRhz3y5XOpsGyXNotuG8XbknO5IuPwScYoxZLCLTgMkpZgS8sWngmyIYVKJFkDWFQ20nSEeqa7EvAiUi8oP2K0RkIrAN+J6IMxWUiOwlIl39ALYDnc2V0Q+oFZFiujElgK4RpGFQyUDbEYKr0De7iiDFIfWMMUZETgXuFJErgEZgDc5M31uBuc4bC9TjzJ3Q1sndPQTcm7SzcHc/A94A1gJL6bw0/ov1eQ0qqyONOHtOPW/a6DM4+4DAz3Vhxyc/g+1/sJ0iVTsIx32zbysVVjcNKqsjBfikBAAKdUdhFnlh5TRl3pkVKENs7yMI3A9UdZevRgBr6HoRf7FaBLGqqGHXvaSe1pbobBNO9UyL7QDpaLQdINNsrxEApHz0k20Nbb7pLP9J+OqfrK/CpsILReCbH+rHTZ/YjhBcbZtsJ0iHb16zqdIiSMOWJt8c8OI/bRttJ0iHbhpkgW+K4KNGLYKs8VcR+OY1myovFEG97QCp2tz4EQmTsB0jeEwTJD6ynSIdukaQBXHbAVLV0NbI+p0bbMcInuaVgK8KNnCrhl4ogg9tB0jHqrr3bEcInpalthOk6wPbATJNiyBNq+retx0heJqX2E6QLi2CLFhnO0A6VtStsh0heJoW206QrtW2A2SaF4rAV2sESz9ewY4W3xwD5X1tm3TTwAO0CNLUalp586OFXS+oUtPwgu0E6WrFZ6/ZVHihCNaz60grnvfqxjdsRwiOhudtJ0jXh4TjgTvpxHoRxKqiCZyBFHxj7qY3aU202o7hf4kGaHzFdop0BW6zADxQBC5frWtva9nO3E1v2o7hfzufAeO7g/RW2g6QDV4pgkW2A6Tr6Zq0BolVHdnxiO0E3THXdoBs0CLopjc2L2DdDt8cFOk9zcugab7tFN3xuu0A2eCVIlhK5wM3eo7BMHvN07Zj+Nf2+20n6I6NhOOBO4YAPFIEsapoA84Y8L7yTM1zbGzw1Xn03tDyHux43HaK7gjkZgF4pAhcL9oOkK7mRAsPrHrUdgz/2XozPlsBbBfIzQLwVhH803aA7vjHuhdYs73Gdgz/aHobGp61naK7tAhy4GV8NoIlQIIEM1c+YDuGP5gEbL3OdoruasaZiDSQPFMEsaroDmCe7RzdMXfTWzy3zndbNrlX/39+facA4HXC8cCOXuuZInD5cvMA4M5l9/FR4xbbMbyrZQ1svcF2ip7w5d7NVHmtCHx34Hm7+tYdzFh6j+0Y3mQS8PElfjyKsJ0BnrAdIpu8VgTz8fGZXa9vms9fVz9lO4b3bPsVNPn6nbe5hOOBPnrMU0Xgznz0F9s5euKeFQ8wf/PbtmN4x84o1N1uO0VPzbYdINs8VQQuXx6A3i5Bgl+8fQsf1q+3HcW+5mWw5WLbKTIh0PsHwINFEKuKLgJW2M7RE/WtO/jft65lS2Mez4zUug42T/PzfoF2bxKOB/5AEc8Vgcv3h+t9uCPO9DeuZmtzne0oude6ATadDm2BWCsK/GYBeLcIfL150O6D+rVcPO/K/JozsXU9bPoatK6xnSQTWoA/2Q6RC54sglhV9H3Ad4PZdWT19rVcMPcKPsyHU5abV8LGU4NSAgCzCMdrbYfIBU8WgetO2wEy5cMd6znn1em8udlXAzGlZ+dzsPEkaPPV6PRdudN2gFzxchFUA/+2HSJT6lt3cNmbv2D2BwEcw6DuLvjou2B8NQZtV+YSjufNeHSeLQL3mIJf286RSW0mwV3Lf8dVC24Mxn6D1jhsOhPqbrGdJBvush0glzxbBK6HgMDtdn9lw1y+/fIP+df6l21H6b76v0DtFGh8yXaSbFhHHhw7kEyMMbYzdKqyOnIbcIntHNlSOexozh/7Xfbpu5ftKKlpXumcStw4x3aSbLqScPwm2yFyyQ9FEALeB0ptZ8mWQikgss+JTBtzBoNLB9mO07HWdVB3G+yYhXMOTmBtB/YjHM+rU0k9XwQAldWRO4DptnNkW0lBCaeO/ApT9z2J4b2H2o7jaFkD2/8A9X/CGZsj8K4mHL/Rdohc80sRDMGZgbav7Sy5UEABk4YfxUn7nMjEIYdSKIW5DWBanTkJ6x+GxhcI+BpAsvXAGMJx3x8Xna4ui0BE2nCGGxecEScvMMZkbOw2EXkIiBpjOj2Us7I6ci3w80w9rl8MKhnIsUOPZNKwozh88HhKCkuy80CJHc70Yw3PQ8O/IJFXa8btvkc4/qDtEDakUgT1xpi+7uUTgSuNMcdlLEDqRdAXeA8YlqnH9puSghIOHTSOsQPGcED5aA4oH82Q7u5TaF0PzUvcj0XQOA8I7EhcqVgCHEo4nrAdxIaiNJfvD3wCICJ9gb8BA4Fi4GpjzN9EpA/wV2BvoBC43hjzmIj8HDgJKMMZDfZck8Z2SawqWl9ZHbkG+G2amQOjKdHEvM1vMW/zp2NoDuhVztDSwQwurWBQSQWDSisoKyz9z+ZEm2ljZ2sDW5o+ZkvTx1w+YtnSwawYR+JjW0/Dqy7L1xKA1IqgTEQW4ey1HwF8wb2+ETjVGLNNRAYD80TkaeDLQNwYUwUgIuXu8ncbY65zr/sTEAGeSTPv74DvAkemebvA2tpcx9bmOlZtez+l5df2WfXx4L71WU7lO88Tjvt2mLxMSOWAogZjzARjzFicP/I/iojg7DP4pYgsAf4F7IWz2r4UOF5EbhGRSmNM+wFBU0TkDRFZilMmB6cb1p1C/QeAzkneTWsay8R2Bo9pBS6zHcK2tI4sNMbMBQYDQ4Az3c+HG2MmABuBUmPMKuBwnEK4SUR+LiKlwEzgNGPMOOB+unlcQKwqugS4ozu3VfBeY1mW9jb61k2E40tsh7AtrSIQkbE42/1bgHJgkzGmRUSmAPu6y4SAncaYPwO3AYfx6R/9R+6+hdN6mPsanLcTVZo+aCwdaDuDhywErrcdwgvS2UcAzubAWcaYNhF5GHhGRN7CmdZ8pbvMOGCGiCRwBnY43xizVUTux1lLWAP06KyuWFW0obI6ch4+Hv7clrVNpXn7rstumoDvEI77bnatbPDFAUV7UlkduR/4vu0cfvPKIW/XiVDe9ZKBdgXheCBPm+wOr5992JWL8eF06rY1GdloO4NlrwMzbIfwEl8XQawquhP4BrDTdhY/qWst2mo7g0U7gbPy+ZiBjvi6CABiVdFlwIW2c/hJbUtJ3h1Ln+QnhOPv2Q7hNb4vAoBYVfQB4M+2c/jF2sbAntHdld8Tjt9nO4QXBaIIXOcDy22H8IP38/NYgrnAj2yH8KrAFEGsKloPVOEc2KQ6sbqxNN/eMYgDUwnH82JAhe4ITBEAxKqia3BObNKdh51Y21TqkVFPcmIHcFK+zE/QXYEqAoBYVfRNnMOfda/wHmxtKx5kDIEae3wPEsA3Ccd1euouBK4IAGJV0acI8ICnmdBsZIPtDDnwE8LxdM9wzUuBLAKAWFX0TvTkpD3a1lYUgIkVOnUV4XhezU3QE4EtAoBYVfQS8mjaqnRsaO4V5P0oPyUc/6XtEH4S6CIAiFVFp6NrBv9lbVNpUPehXEE4frPtEH4T+CKA/6wZ3GY7h5esDuaxBJfriUTdkxdFABCril4G3Go7h1e831jW33aGDLuUcFxPJOqmvCkCgFhV9H+BK8ijgfr3ZG1TSVCOJTDAdMLx220H8bO8KgKAWFX0FuB0IJ9PvGFLa/FgY2i0naOHtgOnEI7faTuI3+VdEQDEqqKzgcnk9eHIIi1G/Hy03WrgGMLxp20HCYK8LAKAWFV0PnAUeTywyfa2Qr9ObvAiMJFwPG9/d5mWt0UAEKuKrgWOBZ6wncWGTS2+PJbgHuBEwnG/lpgn5XURAMSqottiVdGpOKcx+32bOS01TaVttjOkoRk4j3D8AsJxndciw/K+CNrFqqL3AhPJozEN3m8s7WU7Q4oWAkfooCLZo0WQJFYVfQc4AmdqtcB7v7HM69PMtwLXAkcRji+1HSbIfD2ceTZVVkdOBu4G9rGdJVuGFTdtmD122XDbOfbgHZxBRvUU4hzQNYI9iFVFnwYOwjlPwU/b0inb1NJrqDF4bYKPNuBm4HAtgdzRNYIUVFZHJgD3EcBZmF88ZGFNsZiw7Ryu+cBFhONv2A6Sb3SNIAWxqugi4BicwS+32E2TWfVthV54Pu8BpxOOH6UlYIcWQYpiVdFErCo6ExgF3ERAxkXc3FJsc8iyzcBFwEGE47Ms5sh7WgRpilVF62JV0SuB0Tg7E5ssR+oRS8cS7ARuBEYRjv9GJyK1T4ugm2JV0dpYVfRCnEKYiU9PYlrdWFaYw4f7BGdciDGE41cTjm/P4WOrTujOwgyprI5UAGfj7EfY13KclB3bb+viW0auHp/lh1kG/Ab4E+F4IDapgkaLIMMqqyOFwMk48zFOsRynS6FeTeseO2DZ3lm46wRQDdxFOP5CFu5fZZAWQRZVVkcOAb6NM2OzJ9cSCjFtLx2yEBEytYmwBpgF3Es4vjpD96myTIsgRyqrI0cDZwBfB0KW4+zipUPeXlck9GStYCXwOPCEHgTkT1oEOVZZHSkAKoEI8AVgApZ32kYPXLy4vKgt3f0Ei3BO336ccDxvTtQKKi0Cy9ydjJOBL+IUw9hcZ3ho9PJXR5U1TupkkSZgATAPZ1bhuYTj63MSTuWEFoHHVFZHhgCfBca7nz+Lc85D1oYfvyG8+uXjyrce537ZirOdv4D2P3pYpDMJB5sWgQ9UVkeKgDHASGAv9yOUdHkIUIpTFiVA8W53kcCZFXg7UO9+bAXWA+u+NmjTyumhdRuA94G1OvBH/tEiCCB3P0R7KTTHqqL63r3qlBaBUkoPMVZKaREopdAiUEqhRaCUQotAKYUWgVIKLQKlFFoESim0CJRSaBEopdAiUEqhRaCUQotAKYUWgVIKLQKlFFoESim0CJRSaBEopdAiUEqhRaCUQotAKYUWgVIKLQKlFFoESim0CJRSaBEopdAiUEoB/x8hsmRVBSZ4JQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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h6/4Lq/YZSIdjDqL26AOp3ncwUlGEj2DbUtjyDGx5GupfAhoLv4zydzvR1MW2QxRSkArAo8C3bOcohcNeZfZliZ7jO37vaOpOnUBV/xJfM9S8ATY/CRunQ8NbpV22XY3AIKKpz2wHKZRAFIBYIj4aeNN2jlLYu9tQzhx0Sn2s14EdpLoMduFsnQcbpsLmJyAcRyl/SzTl9tmlaYJSAP6KdzlnYA3ovAcXDPsPYv3K9IhUw/uw7mbY8nfbSYptAxAlmlprO0ghOF8A/JN+5trOUSw9a3tw/rCzOHa3o6iqcGDU663/hLU3wNbXbScppmuIpgLRd0AQCsDzwHjbOYrh+N0ncMnw8+hS7dhQAaYZNt7rFQKzxXaaYvgCGEA0tdV2kHw5XQBiifh44HnbOQqtV21Prtr3Esb1GWs7Sn4al8Cay2HrK7aTFMP5RFN/th0iX64fMgvcWG9je+3PvYdPcX/lB6geAH1mQLcrbScphitIRsR2iHw52wLwu/deRhmN6pOvUweexEV7nevGtn62Nv8NVv8IzGbbSQrpJKKpJ2yHyIfLLYDTCMjKXyVVXL3vj7lk+HnBXPkBOk6EvjOhcnfbSQrpKtsB8uVyAfi+7QCFUFNRzeQDfsakPY62HaX4aoZD38ehapDtJIVyCMnIIbZD5MPJAhBLxEcAznfcWFNRzU0HXMe4Ps6/lcxVRaDvo1A12HaSQjnTdoB8OFkACMC3f6VUcsOYaziw92jbUUqvsg/0+QtU9bedpBBOdHlnoHMFIJaIV+N41QW4fMR/BmNPf66qdoXe94M43+NWBDjQdohc7bQAiEiTiMwTkfki8i8RKeg2j4hME5FTsnhJHOhdyAyl9u0Bcb4ZPdZ2DPuqB0Ov/8brc8NpJ9sOkKtMWgBbjDGjjDH7AT/F/vBJ51hefl7G9NyPS/Y+z3aM8lF3FHS/znaKfJ1kO0Cust0E6Ap8CSAinUXkWb9VsFBETvSf7yQiCb/F8LaInOo//18i8ob/3J0iknXZjyXidTgyuGdbdqnpzvWjr6Kqogyu4isnXS+COqd74B5GMuLk4DOZFIA6fxNgEfBnvJF2AOqBk40xo4EjgVv9lfo4IGWM2c8YMwJouTxsijFmrP9cHV5TPluH4/Cx/ytG/pDuNaEchXrnevwKKpwejMfJzYBsNgH2wlu57/NXdAEmi8gC4B/AbkBfYCEwQURuFpGYMWadP58jReQ1EVkIHAXsk0NeZ7/9J0SO4PByvZS3HFT2gV0m206Rj5NsB8hFVpsAxphXgF54O+HO8H+OMcaMApYDHYwxHwBj8ArBTX7TvwNwG3CKMWYkMJXcvsmd3HPWvaYbl+1zoe0Y5a/TiVDnbLcOB5CMOHeaY1YFQET2AiqB1UA3YIUxplFEjgT6+9NEgM3GmAeAW/B6VG1Z2VeJSGcgm73+AMQS8V3JrdVg3blDTqNbTVfbMdzQ/Toc7axagBNth8hWJp90nYjM8+8LcLYxpklEpgNPisg/gXnAIn+akcBvRKQZrw+1i40xa0VkKl6rYAnwRg5ZD8/hNdZFOvbTQ37ZqB4Anc+CjffYTpKL44A/2Q6RDWeuBowl4n/EwT7/fz7qSibsdoTtGG5pWgmpcS5eOfgZ0VTUdohsuHQmYLt94ZerPbv056hIzHYM91T2hi4/sJ0iF3uQjHS3HSIbThSAWCLeFdjXdo5sfXfgSVSIEx9x+el8Lo7uC3Dq/9SV/86xuJMVgK7VXZgQcXK3RXmo6gcdv2k7RS60ABSBc4N9Ttz9G9RW1tqO4bbOp9tOkAstAEXgXA8SE3efYDuC+zocCpVO7VMDLQBF4VQBiHTsx6CuA2zHCIaOzp38OcKlocRdCepUATis70G2IwRHnXMFoBMO/b+6UgD2tB0gG4dqASic2oNAnDuL0pnNgLIvALFEvC/gzNA4dZV17LvLcNsxgkOqoc65E6m0ABSQM80pgGHdBun1/oVWs7/tBNlypsWqBaDAhnULTG+35aNmpO0E2eppO0CmtAAU2FAtAIWnBaBoXCgAEdsBsjG0m1P1yg0VXaFqgO0U2dACUEBOnU7Xr87pDovLl1tDivWwHSBTLhSAatsBMtW5qhMdKp3tsrC8Vfa1nSAb3V05GciFkDW2A2SqZwenO7Usb24VAMGRzmu1ABRQz1pnWn7uqexjO0G2tAAUiDObAJ2qOtqOEFwVzg0h5sS+KxcKgDMtgEqptB0hwJz7bLUFUCDOFABVRNkPJGWbtgAKxJlNgCbTZDtCcJltthNkq9l2gEy4UACcaQFsaaq3HSG4zBbbCbK12naATLhQALbaDpCpNVu/tB0huJqW206QjWb8QXTLnQsFwJm//Op6J/7mbmpaYTtBNr4kmtJNgAJxpgCsa1xPQ1Oj7RjB1PSF7QTZcKL5D24UAKf+8ivrV9mOEExNy2wnyIYWgAJypgUA8OH6j21HCJ7mLdC42HaKbDjzLeBCAXCqBfDB+o9sRwiexndx5KhaC20BFJBTLYD31zn1TeWGhoW2E2RLC0ABOdUCeH/tYpqNU99W5a9hnu0E2dJNgAJyqgWwrnG9tgIKyTTDltm2U2RLWwCFMmfSrI3AJts5svHy8tdtRwiOhgXQ7NQ5AABLbAfIVNkXAN+HtgNkY+6K12xHCI4tT9tOkIt/2g6QKVcKwDzbAbKxeP0nLNvs1JZL+drylO0E2fqIaMqZU0K1ABTJU0ufsx3BfQ1vQ+N7tlNky5lvf9ACUDSzPntaLw/O18bpthPkQgtAEcwDjO0Q2Vi+ZaXuDMxH83rY9IjtFLl4w3aAbDhRAOZMmrUOeN92jmw9/MkTtiO4a+ODYJw6+APe6Yr/sh0iG04UAN/LtgNka96at3lz1XzbMdzTvAnW/8l2ily8TzS1wXaIbLhUAF6yHSAXty+aZjuCezbcCc3OnEyXzqntf3CrADjXAgBYtO5Dnl8213YMdzSthvW32U6RKy0ARfQ+sNJ2iFzcueg+tjY507OZXetudXHbv4VTOwDBoQIwZ9IsA8y0nSMXn29OcdcHTh7SKq36V2DjNNspcrUKLQBF5+RxIYCHPn6Mt79cZDtG+WreBGt+YjtFPh4lmnKu73LXCsCzONLbamsGw+T5v9NNgfasnQzbPrWdIh8P2Q6QC6cKwJxJsxqBx23nyNVnm5Zy96L/Xm87R9nZPAs23mM7RT6WAS/aDpELpwqAz9nNgEO6rJt/Yedf17P+TttRykfDO7D6x7ZT5OsRV7oBb63KdoAc/ANYC3S3GyM7V0SSL5zQY9WhIlSx9hdQPRTqxtuOZVfTKlh5jouj/rT2F9sBcuVcC2DOpFkNgDPn2HasaNrw4NB3Xjmx56ojRFoKbjOsusj79gur5k2w8vvQtNR2knx9hqPnqICDBcDnxGbAkA6bP3py7wUrd6/dOu7ffmnWw4pTocG5Sxzy17wFVp4NDc6dN9OWh4mmnLpQLZ2rBeBpYJ3tEDvynZ4rXr5r8KJ+NRVmz3Ynal4DK04JV0ugeROsPAu2Ovul2ZqzzX9wtAD4mwH3287RlkpM4/8d+MELl0Y+P0SETjt9QfNqWPEd7ySYoGtaCSu+F6SV/2OiKedO/knnZAHw3QqUVY8bvasbvnhy+IL3RnfeeERWL2xe620ObHygOMHKQcNC+GIiNLxpO0kh3Wc7QL6cLQBzJs1aAsywnaPFYV3Wzntk2NuVXSqb9s1tDttgzVWw5lowARtgdNNMWH4SNKVsJymkzYCT1yync7YA+H5tOwDAVbt9Onty/49HVgi9857Zxntg+YnQ+EEBklnWvMEraqsvCsKhvtbuJppy8prldGKMszswAYgl4s8AE2wsu1NF0/q7h7z3bqSm4eDCz70Gul8JXS4EcfB0jfo5sPonQTjM15YmYDDR1BLbQfLlegsA4GYbCx3aYfPimXsvWF2clR+gAdbeCMtPgK0OHS7b9gWsvsLbpxHMlR+8Q39LbIcoBOdbAACxRPxNYHSplndar+UvXdxv6aiM9vIXSt2x0O1qqBlWskVmpXmt15HHhruC2NxPZ4BRRFMLdjahiPQDfg+MBbbijRh0mTEmq+07ETkHeNoYk9VOFBG5HthojLmlvWmC0AIA+E0pFlKJaZyy5/sv/ueuSw8t6coP3gAZX3wDVl9aXucNNK2Gdb+D1CGwfkrQV36AGRmu/AI8Bsw2xgwyxgwHrgH65rDMc4BIO8upzGF+X3Fw47JNM4DJwMBiLaBPdcOye4e8t6pzZdPhxVrGzjV7XWVvegRqD4bOZ0LH40E6lD5K/euwabq3h5/QXOLcBPw8w2mPBBqNMbe3PGGMmQcgIlcC3wVqgceMMT8XkQHA34C5wCHAUuBEYBJwADBdRLYA44D3gLuBY4ApItIFuACoARYDZxljNmcSMhAtgDmTZjUB1xZr/rGua9+aMeztqs6VTSOLtYysbX0VVl8Cn+8Hqy6GTY95zfBiMY3ejr0v/wuWHgwrToJNMwjRyg8wnWgq015dRgD/dtKDiBwDDAEOBEYBY0Sk5UtlCPAnY8w+eBe8fdsY8wheX4NnGGNGGfNVE6veGHOYMeYh4P8ZY8YaY/bDKw4/yPQNBaUFwJxJsx6MJeLnAUcVcr5X77Zk9vG7rImJkFdTq2jMBtj8hHejEmoPgJoxULMv1IyE6hwbRU2rvZF5GxZAw3yof9m7fiG8GoH/U4D5HOPf3vIfd8Zb8ZPAJy2tBLziMWAH80k/BXmEiNyAd4VsZyDjARUDUwB8PwTm4zWF8tKpomn9PUPee2/XmobxeacqmSbY+pp3ayFdoGoPqOwDlX29nxVd8f704r3GbIGmFdC03L8t836qdFOIpj7OYvp3gFPaeF6Am4wxd2z3pLcJkN6cagLqdjD/9J5TpwEnGWPm+zsMx2caMlAFYM6kWYtiifitwE/zmc9edZs+vG3PD6qrK8xBBYpmj9kAje96N5Wrj4CfZfma54DJInK+MWYqgIiMBdYD3xeR6caYjSKyG17rYkc2AF128PsuwDIRqQbOwNt/kJFAFQDfDcDpQP9cXnx6ry9euqhfan8ROhY2lnKUAX5ANJXRTrWvXmSMEZGTgd+LyNVAPf5hQLzt+1e8AwVsBM5kx9e1TANuT9sJ2Np1wGvAp8BCdlwsthOI8wBaiyXiJ5Jl34FV0tzwh4Efvjqy0yaLe/lVGbqNaOqHtkMUSyALAEAsEZ8JfDOTaftWb102bch7qztXNo8ocizllk+BEURTG20HKZZAHAZsx6XATs9KOaLrl/96eNg71bryqzacH+SVHwJcAPzLhXdw0oYx1+6+ZPYvo5/sVyH0KlUu5Yy7iKaesR2i2IK4EzDdLcA3gGPTn+xcsW3dPUPee79fTeN4K6lUuVsKXG47RCkEdh9Ai1gi3huYh38u9d51mz74054f1FZXmJyOEqhQiBNNJWyHKIXAbgK0mDNp1kq8w4JNZ/b+4qU7Br2/h678agemhmXlhxC0AFo8v3D4D4/stnaK7RyqrD0HHEc0FbA+2doXmgIAQDLyMPAd2zFUWVoEjCOaWms7SCkFfhOglXOBnV7LrUJnJXB82FZ+CFsBiKY24V1jvdp2FFU26oETiaY+sR3EhnAVAMDvy+07QIPlJMo+A5xDNBWCUVnaFr4CABBNPY93qaYWgXC7jmjK6aG98hWunYCtJSNx4FEK0H+Acs40oqlzbYewLZwtgBbR1Czg22hLIGyew+tDL/TCXQCgpQh8i5B1bhdiM/HO9AvNsf4d0QIA+Gd+aREIvruAbxFNBb7v8kyFex9Aa8nIRLy+3GttR1EFdyPRVLbdegWeFoDWtAgETTPwY6IpPQ28DVoA2pKMHAM8DHSzHUXlpQE4k2iqbIaRLzdaANqTjAwGHgH2sx1F5WQ9cDLR1HO2g5Qz3QnYnmhqMV4PrNMsJ1HZWw6M15V/57QFkIlk5Hzgj+h+ARe8ApxGNPWp7SAu0BZAJqKpqXgDNobyghFHNAM3AYfryp85bQFkIxnZBbgfb8RWVT6WAWcRTT1rO4hrtAWQjWjqS7yxBn6G942j7HsC2E9X/txoCyBXychhwJ+AfW1HCak1wKVEU9NtB3GZtgByFU3NBUbjjUi8xnKasHkC2EdX/vxpC6AQkpGeeIOSXoAW1WJKAlcTTT1oO0hQaAEopGRkFPAHIGY5SdAsB24E7iCa0ku3C0gLQDEkI6cDvwZ2sx3FcWvxPsc/+P05qgLTAlAsyUgn4Frgx0BHy2lcswmvJfXrMPbUW0paAIrN2z9wMXAJ0NdymnK3FbgDmEw0tdx2mDDQAlAqyUgtcBbwE2Bvy2nKzTrgQeAmoqmk7TBhogWg1JIRwRux+EK8MQqq7Qayphl4BrgXeFx76bFDC4BNyUg/4PvA+cAAu2FK5l28lf4BoqmU7TBhpwWgHCQjFXgXG00EjgP2B8RqpsJag9fEv5do6g3bYdTXtACUI69lcCxeQTga6GE3UNaagXeAuXjN/IQevy9PWgDKXTJSCRyIVwwmAmMov9ZBPfA68BLeSv+yHr5zgxYA1yQjvYARwLBWtwFAZYlSLAdew1vZ5wJv6je8m7QABEUyUgMMxisGQ/2fg4FOeEOf1fo/02+1eEchWloU9XhDZa/yf67Eu9Z+yXY3PSsvMLQAKEhGqoFKoql621FUaWkBUCrE9NJVpUJMC4BSIaYFQKkQ0wKgVIhpAVAqxLQAKBViWgCUCjEtAEqFmBYApUJMC4BSIaYFQKkQ0wKgVIhpAVAqxLQAKBViWgCUCjEtAEqFmBYApUJMC4BSIaYFQKkQ0wKgVIhpAVAqxLQAKBViWgCUCjEtAEqFmBYApUJMC4BSIaYFQKkQ+/893DU+AiIM2gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_label = ['unknow'] + source_region\n",
    "for i in range(len(x_label)):\n",
    "    X = []\n",
    "    try:\n",
    "        for j in neuron_source[i]:\n",
    "            X.append(neuron_information[j]['axonal_projection_terminal_combined'])\n",
    "        Y = np.sum(X,axis=0)/np.sum(X)\n",
    "        Y = [np.sum(Y[:1]),np.sum(Y[1:5]),np.sum(Y[5:])]\n",
    "        plt.pie(Y,labels=['Lateral','Basal','Central'],radius=1,startangle=90,colors=color_pool)\n",
    "        plt.pie([100],colors=['w'],radius=0.5)\n",
    "        plt.title(x_label[i]+' neurons axson projection',fontsize=10)\n",
    "        plt.show()\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04a3a094e6424d5bb73f1670be155f10",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "iteration on neurons (generating shaft) ...:   0%|          | 0/9 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = []\n",
    "Y = []\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "for i in tool_bar(neuron_source,desc='iteration on neurons (generating shaft) ...',leave=False,colour='#55AF7B'):\n",
    "    neuronvis.clear()\n",
    "    for j in neuron_source[i]:\n",
    "        X.append(neuron_information[j]['soma_position'].copy())\n",
    "        if X[-1][2]>5700:\n",
    "            X[-1][2] = 11400-X[-1][2]\n",
    "        Y.append(color_pool[0].tolist())\n",
    "draw_points(neuronvis,X,Y)\n",
    "neuronvis.render.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "307fc1a97d98458782934a3369092e09",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "iteration on neurons (generating shaft) ...:   0%|          | 0/9 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.0,1.0,1.0))\n",
    "for i in tool_bar(neuron_source,desc='iteration on neurons (generating shaft) ...',leave=False,colour='#55AF7B'):\n",
    "    if i==1:\n",
    "        continue\n",
    "    neuronvis.clear()\n",
    "    print(len(neuron_source[i]))\n",
    "    for j in (np.random.choice(neuron_source[i],25) if len(neuron_source[i])>25 else neuron_source[i]):\n",
    "        control_console_output(0)\n",
    "        neuronvis.addNeuronByID(j[:-7],j[-7:],color=color_pool[i],somaHide=True,dendriteHide=True,mirrorToRight=True)\n",
    "        control_console_output(1)\n",
    "    for j in ['anterior','posterior','left','right','dorsal','ventral']:\n",
    "        set_view(neuronvis,j)\n",
    "        neuronvis.render.savepng(x_label[i]+'_'+j+'.png')\n",
    "neuronvis.render.closeWindow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('CX')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "6684bfb8f990107dc2e41da7bb6829210ca36b987584a9d9d8b153337675e487"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
